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id="toc-Challenges" class="vector-toc-list-item vector-toc-level-3"> <a class="vector-toc-link" href="#Challenges"> <div class="vector-toc-text"> 4.1.1 Challenges </div> </a> <ul id="toc-Challenges-sublist" class="vector-toc-list"> </ul> </li> </ul> </li> </ul> </li> <li id="toc-Hardware" class="vector-toc-list-item vector-toc-level-1"> <a class="vector-toc-link" href="#Hardware"> <div class="vector-toc-text"> 5 Hardware </div> </a> <ul id="toc-Hardware-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Applications" class="vector-toc-list-item vector-toc-level-1"> <a class="vector-toc-link" href="#Applications"> <div class="vector-toc-text"> 6 Applications </div> </a> <button aria-controls="toc-Applications-sublist" class="cdx-button cdx-button--weight-quiet cdx-button--icon-only vector-toc-toggle"> Toggle Applications subsection </button> <ul id="toc-Applications-sublist" class="vector-toc-list"> <li id="toc-Automatic_speech_recognition" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#Automatic_speech_recognition"> <div class="vector-toc-text"> 6.1 Automatic speech recognition </div> </a> <ul id="toc-Automatic_speech_recognition-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Image_recognition" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#Image_recognition"> <div class="vector-toc-text"> 6.2 Image recognition </div> </a> <ul id="toc-Image_recognition-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Visual_art_processing" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#Visual_art_processing"> <div class="vector-toc-text"> 6.3 Visual art processing </div> </a> <ul id="toc-Visual_art_processing-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Natural_language_processing" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#Natural_language_processing"> <div class="vector-toc-text"> 6.4 Natural language processing </div> </a> <ul id="toc-Natural_language_processing-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Drug_discovery_and_toxicology" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#Drug_discovery_and_toxicology"> <div class="vector-toc-text"> 6.5 Drug discovery and toxicology </div> </a> <ul id="toc-Drug_discovery_and_toxicology-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Customer_relationship_management" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#Customer_relationship_management"> <div class="vector-toc-text"> 6.6 Customer relationship management </div> </a> <ul id="toc-Customer_relationship_management-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Recommendation_systems" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#Recommendation_systems"> <div class="vector-toc-text"> 6.7 Recommendation systems </div> </a> <ul id="toc-Recommendation_systems-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Bioinformatics" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#Bioinformatics"> <div class="vector-toc-text"> 6.8 Bioinformatics </div> </a> <ul id="toc-Bioinformatics-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Deep_Neural_Network_Estimations" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#Deep_Neural_Network_Estimations"> <div class="vector-toc-text"> 6.9 Deep Neural Network Estimations </div> </a> <ul id="toc-Deep_Neural_Network_Estimations-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Medical_image_analysis" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#Medical_image_analysis"> <div class="vector-toc-text"> 6.10 Medical image analysis </div> </a> <ul id="toc-Medical_image_analysis-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Mobile_advertising" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#Mobile_advertising"> <div class="vector-toc-text"> 6.11 Mobile advertising </div> </a> <ul id="toc-Mobile_advertising-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Image_restoration" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#Image_restoration"> <div class="vector-toc-text"> 6.12 Image restoration </div> </a> <ul id="toc-Image_restoration-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Financial_fraud_detection" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#Financial_fraud_detection"> <div class="vector-toc-text"> 6.13 Financial fraud detection </div> </a> <ul id="toc-Financial_fraud_detection-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Materials_science" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#Materials_science"> <div class="vector-toc-text"> 6.14 Materials science </div> </a> <ul id="toc-Materials_science-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Military" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#Military"> <div class="vector-toc-text"> 6.15 Military </div> </a> <ul id="toc-Military-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Partial_differential_equations" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#Partial_differential_equations"> <div class="vector-toc-text"> 6.16 Partial differential equations </div> </a> <ul id="toc-Partial_differential_equations-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Deep_backward_stochastic_differential_equation_method" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#Deep_backward_stochastic_differential_equation_method"> <div class="vector-toc-text"> 6.17 Deep backward stochastic differential equation method </div> </a> <ul id="toc-Deep_backward_stochastic_differential_equation_method-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Image_reconstruction" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#Image_reconstruction"> <div class="vector-toc-text"> 6.18 Image reconstruction </div> </a> <ul id="toc-Image_reconstruction-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Weather_prediction" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#Weather_prediction"> <div class="vector-toc-text"> 6.19 Weather prediction </div> </a> <ul id="toc-Weather_prediction-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Epigenetic_clock" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#Epigenetic_clock"> <div class="vector-toc-text"> 6.20 Epigenetic clock </div> </a> <ul id="toc-Epigenetic_clock-sublist" class="vector-toc-list"> </ul> </li> </ul> </li> <li id="toc-Relation_to_human_cognitive_and_brain_development" class="vector-toc-list-item vector-toc-level-1"> <a class="vector-toc-link" href="#Relation_to_human_cognitive_and_brain_development"> <div class="vector-toc-text"> 7 Relation to human cognitive and brain development </div> </a> <ul id="toc-Relation_to_human_cognitive_and_brain_development-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Commercial_activity" class="vector-toc-list-item vector-toc-level-1"> <a class="vector-toc-link" href="#Commercial_activity"> <div class="vector-toc-text"> 8 Commercial activity </div> </a> <ul id="toc-Commercial_activity-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Criticism_and_comment" class="vector-toc-list-item vector-toc-level-1"> <a class="vector-toc-link" href="#Criticism_and_comment"> <div class="vector-toc-text"> 9 Criticism and comment </div> </a> <button aria-controls="toc-Criticism_and_comment-sublist" class="cdx-button cdx-button--weight-quiet cdx-button--icon-only vector-toc-toggle"> Toggle Criticism and comment subsection </button> <ul id="toc-Criticism_and_comment-sublist" class="vector-toc-list"> <li id="toc-Theory" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#Theory"> <div class="vector-toc-text"> 9.1 Theory </div> </a> <ul id="toc-Theory-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Errors" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#Errors"> <div class="vector-toc-text"> 9.2 Errors </div> </a> <ul id="toc-Errors-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Cyber_threat" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#Cyber_threat"> <div class="vector-toc-text"> 9.3 Cyber threat </div> </a> <ul id="toc-Cyber_threat-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Data_collection_ethics" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#Data_collection_ethics"> <div class="vector-toc-text"> 9.4 Data collection ethics </div> </a> <ul id="toc-Data_collection_ethics-sublist" class="vector-toc-list"> </ul> </li> </ul> </li> <li id="toc-See_also" class="vector-toc-list-item vector-toc-level-1"> <a class="vector-toc-link" href="#See_also"> <div class="vector-toc-text"> 10 See also </div> </a> <ul id="toc-See_also-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-References" class="vector-toc-list-item vector-toc-level-1"> <a class="vector-toc-link" href="#References"> <div class="vector-toc-text"> 11 References </div> </a> <ul id="toc-References-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Further_reading" class="vector-toc-list-item vector-toc-level-1"> <a class="vector-toc-link" href="#Further_reading"> <div class="vector-toc-text"> 12 Further reading </div> </a> <ul id="toc-Further_reading-sublist" class="vector-toc-list"> </ul> </li> </ul> </div> </div> </nav> </div> </div> <div class="mw-content-container"> <main id="content" class="mw-body"> <header class="mw-body-header vector-page-titlebar"> <nav aria-label="Contents" class="vector-toc-landmark"> <div id="vector-page-titlebar-toc" class="vector-dropdown vector-page-titlebar-toc vector-button-flush-left" title="Table of Contents" > <input type="checkbox" id="vector-page-titlebar-toc-checkbox" role="button" aria-haspopup="true" data-event-name="ui.dropdown-vector-page-titlebar-toc" class="vector-dropdown-checkbox " aria-label="Toggle the table of contents" > <label id="vector-page-titlebar-toc-label" for="vector-page-titlebar-toc-checkbox" class="vector-dropdown-label cdx-button cdx-button--fake-button cdx-button--fake-button--enabled cdx-button--weight-quiet cdx-button--icon-only " aria-hidden="true" > Toggle the table of contents </label> <div class="vector-dropdown-content"> <div id="vector-page-titlebar-toc-unpinned-container" class="vector-unpinned-container"> </div> </div> </div> </nav> <h1 id="firstHeading" class="firstHeading mw-first-heading">Deep learning</h1> <div id="p-lang-btn" class="vector-dropdown mw-portlet mw-portlet-lang" > <input type="checkbox" id="p-lang-btn-checkbox" role="button" aria-haspopup="true" data-event-name="ui.dropdown-p-lang-btn" class="vector-dropdown-checkbox mw-interlanguage-selector" aria-label="Go to an article in another language. Available in 58 languages" > <label id="p-lang-btn-label" for="p-lang-btn-checkbox" class="vector-dropdown-label cdx-button cdx-button--fake-button cdx-button--fake-button--enabled cdx-button--weight-quiet cdx-button--action-progressive mw-portlet-lang-heading-58" aria-hidden="true" > 58 languages </label> <div class="vector-dropdown-content"> <div class="vector-menu-content"> <ul class="vector-menu-content-list"> <li class="interlanguage-link interwiki-af mw-list-item"><a href="https://af.wikipedia.org/wiki/Diepleer" title="Diepleer – Afrikaans" lang="af" hreflang="af" data-title="Diepleer" data-language-autonym="Afrikaans" data-language-local-name="Afrikaans" class="interlanguage-link-target">Afrikaans</a></li><li class="interlanguage-link interwiki-ar mw-list-item"><a href="https://ar.wikipedia.org/wiki/%D8%AA%D8%B9%D9%84%D9%85_%D9%85%D8%AA%D8%B9%D9%85%D9%82" title="تعلم متعمق – Arabic" lang="ar" hreflang="ar" data-title="تعلم متعمق" data-language-autonym="العربية" data-language-local-name="Arabic" class="interlanguage-link-target">العربية</a></li><li class="interlanguage-link interwiki-az mw-list-item"><a href="https://az.wikipedia.org/wiki/D%C9%99rin_%C3%B6yr%C9%99nm%C9%99" title="Dərin öyrənmə – Azerbaijani" lang="az" hreflang="az" data-title="Dərin öyrənmə" data-language-autonym="Azərbaycanca" data-language-local-name="Azerbaijani" class="interlanguage-link-target">Azərbaycanca</a></li><li class="interlanguage-link interwiki-bn mw-list-item"><a href="https://bn.wikipedia.org/wiki/%E0%A6%97%E0%A6%AD%E0%A7%80%E0%A6%B0_%E0%A6%B6%E0%A6%BF%E0%A6%96%E0%A6%A8" title="গভীর শিখন – Bangla" lang="bn" hreflang="bn" data-title="গভীর শিখন" data-language-autonym="বাংলা" data-language-local-name="Bangla" class="interlanguage-link-target">বাংলা</a></li><li class="interlanguage-link interwiki-zh-min-nan mw-list-item"><a href="https://zh-min-nan.wikipedia.org/wiki/Chhim-t%C5%8D%CD%98_ha%CC%8Dk-si%CC%8Dp" title="Chhim-tō͘ ha̍k-si̍p – Minnan" lang="nan" hreflang="nan" data-title="Chhim-tō͘ ha̍k-si̍p" data-language-autonym="閩南語 / Bân-lâm-gú" data-language-local-name="Minnan" class="interlanguage-link-target">閩南語 / Bân-lâm-gú</a></li><li class="interlanguage-link interwiki-bg mw-list-item"><a href="https://bg.wikipedia.org/wiki/%D0%94%D1%8A%D0%BB%D0%B1%D0%BE%D0%BA%D0%BE_%D0%BE%D0%B1%D1%83%D1%87%D0%B5%D0%BD%D0%B8%D0%B5" title="Дълбоко обучение – Bulgarian" lang="bg" hreflang="bg" data-title="Дълбоко обучение" data-language-autonym="Български" data-language-local-name="Bulgarian" class="interlanguage-link-target">Български</a></li><li class="interlanguage-link interwiki-bs mw-list-item"><a href="https://bs.wikipedia.org/wiki/Duboko_u%C4%8Denje" title="Duboko učenje – Bosnian" lang="bs" hreflang="bs" data-title="Duboko učenje" data-language-autonym="Bosanski" data-language-local-name="Bosnian" class="interlanguage-link-target">Bosanski</a></li><li class="interlanguage-link interwiki-ca mw-list-item"><a href="https://ca.wikipedia.org/wiki/Aprenentatge_profund" title="Aprenentatge profund – Catalan" lang="ca" hreflang="ca" data-title="Aprenentatge profund" data-language-autonym="Català" data-language-local-name="Catalan" class="interlanguage-link-target">Català</a></li><li class="interlanguage-link interwiki-cs mw-list-item"><a href="https://cs.wikipedia.org/wiki/Hlubok%C3%A9_u%C4%8Den%C3%AD" title="Hluboké učení – Czech" lang="cs" hreflang="cs" data-title="Hluboké učení" data-language-autonym="Čeština" data-language-local-name="Czech" class="interlanguage-link-target">Čeština</a></li><li class="interlanguage-link interwiki-da mw-list-item"><a href="https://da.wikipedia.org/wiki/Deep_learning" title="Deep learning – Danish" lang="da" hreflang="da" data-title="Deep learning" data-language-autonym="Dansk" data-language-local-name="Danish" class="interlanguage-link-target">Dansk</a></li><li class="interlanguage-link interwiki-de mw-list-item"><a href="https://de.wikipedia.org/wiki/Deep_Learning" title="Deep Learning – German" lang="de" hreflang="de" data-title="Deep Learning" data-language-autonym="Deutsch" data-language-local-name="German" class="interlanguage-link-target">Deutsch</a></li><li class="interlanguage-link interwiki-et mw-list-item"><a href="https://et.wikipedia.org/wiki/S%C3%BCgav%C3%B5pe" title="Sügavõpe – Estonian" lang="et" hreflang="et" data-title="Sügavõpe" data-language-autonym="Eesti" data-language-local-name="Estonian" class="interlanguage-link-target">Eesti</a></li><li class="interlanguage-link interwiki-el mw-list-item"><a href="https://el.wikipedia.org/wiki/%CE%92%CE%B1%CE%B8%CE%B9%CE%AC_%CE%BC%CE%AC%CE%B8%CE%B7%CF%83%CE%B7" title="Βαθιά μάθηση – Greek" lang="el" hreflang="el" data-title="Βαθιά μάθηση" data-language-autonym="Ελληνικά" data-language-local-name="Greek" class="interlanguage-link-target">Ελληνικά</a></li><li class="interlanguage-link interwiki-es mw-list-item"><a href="https://es.wikipedia.org/wiki/Aprendizaje_profundo" title="Aprendizaje profundo – Spanish" lang="es" hreflang="es" data-title="Aprendizaje profundo" data-language-autonym="Español" data-language-local-name="Spanish" class="interlanguage-link-target">Español</a></li><li class="interlanguage-link interwiki-eo mw-list-item"><a href="https://eo.wikipedia.org/wiki/Deep_learning" title="Deep learning – Esperanto" lang="eo" hreflang="eo" data-title="Deep learning" data-language-autonym="Esperanto" data-language-local-name="Esperanto" class="interlanguage-link-target">Esperanto</a></li><li class="interlanguage-link interwiki-eu mw-list-item"><a href="https://eu.wikipedia.org/wiki/Ikaskuntza_sakon" title="Ikaskuntza sakon – Basque" lang="eu" hreflang="eu" data-title="Ikaskuntza sakon" data-language-autonym="Euskara" data-language-local-name="Basque" class="interlanguage-link-target">Euskara</a></li><li class="interlanguage-link interwiki-fa mw-list-item"><a href="https://fa.wikipedia.org/wiki/%DB%8C%D8%A7%D8%AF%DA%AF%DB%8C%D8%B1%DB%8C_%D8%B9%D9%85%DB%8C%D9%82" title="یادگیری عمیق – Persian" lang="fa" hreflang="fa" data-title="یادگیری عمیق" data-language-autonym="فارسی" data-language-local-name="Persian" class="interlanguage-link-target">فارسی</a></li><li class="interlanguage-link interwiki-fr mw-list-item"><a href="https://fr.wikipedia.org/wiki/Apprentissage_profond" title="Apprentissage profond – French" lang="fr" hreflang="fr" data-title="Apprentissage profond" data-language-autonym="Français" data-language-local-name="French" class="interlanguage-link-target">Français</a></li><li class="interlanguage-link interwiki-ga mw-list-item"><a href="https://ga.wikipedia.org/wiki/Domhainfhoghlaim" title="Domhainfhoghlaim – Irish" lang="ga" hreflang="ga" data-title="Domhainfhoghlaim" data-language-autonym="Gaeilge" data-language-local-name="Irish" class="interlanguage-link-target">Gaeilge</a></li><li class="interlanguage-link interwiki-gl mw-list-item"><a href="https://gl.wikipedia.org/wiki/Deep_learning" title="Deep learning – Galician" lang="gl" hreflang="gl" data-title="Deep learning" data-language-autonym="Galego" data-language-local-name="Galician" class="interlanguage-link-target">Galego</a></li><li class="interlanguage-link interwiki-ko mw-list-item"><a href="https://ko.wikipedia.org/wiki/%EB%94%A5_%EB%9F%AC%EB%8B%9D" title="딥 러닝 – Korean" lang="ko" hreflang="ko" data-title="딥 러닝" data-language-autonym="한국어" data-language-local-name="Korean" class="interlanguage-link-target">한국어</a></li><li class="interlanguage-link interwiki-hy mw-list-item"><a href="https://hy.wikipedia.org/wiki/%D4%BD%D5%B8%D6%80_%D5%B8%D6%82%D5%BD%D5%B8%D6%82%D6%81%D5%B8%D6%82%D5%B4" title="Խոր ուսուցում – Armenian" lang="hy" hreflang="hy" data-title="Խոր ուսուցում" data-language-autonym="Հայերեն" data-language-local-name="Armenian" class="interlanguage-link-target">Հայերեն</a></li><li class="interlanguage-link interwiki-hi mw-list-item"><a href="https://hi.wikipedia.org/wiki/%E0%A4%A1%E0%A5%80%E0%A4%AA_%E0%A4%B2%E0%A4%B0%E0%A5%8D%E0%A4%A8%E0%A4%BF%E0%A4%82%E0%A4%97" title="डीप लर्निंग – Hindi" lang="hi" hreflang="hi" data-title="डीप लर्निंग" data-language-autonym="हिन्दी" data-language-local-name="Hindi" class="interlanguage-link-target">हिन्दी</a></li><li class="interlanguage-link interwiki-id mw-list-item"><a href="https://id.wikipedia.org/wiki/Pemelajaran_dalam" title="Pemelajaran dalam – Indonesian" lang="id" hreflang="id" data-title="Pemelajaran dalam" data-language-autonym="Bahasa Indonesia" data-language-local-name="Indonesian" class="interlanguage-link-target">Bahasa Indonesia</a></li><li class="interlanguage-link interwiki-it mw-list-item"><a href="https://it.wikipedia.org/wiki/Apprendimento_profondo" title="Apprendimento profondo – Italian" lang="it" hreflang="it" data-title="Apprendimento profondo" data-language-autonym="Italiano" data-language-local-name="Italian" class="interlanguage-link-target">Italiano</a></li><li class="interlanguage-link interwiki-he mw-list-item"><a href="https://he.wikipedia.org/wiki/%D7%9C%D7%9E%D7%99%D7%93%D7%94_%D7%A2%D7%9E%D7%95%D7%A7%D7%94" title="למידה עמוקה – Hebrew" lang="he" hreflang="he" data-title="למידה עמוקה" data-language-autonym="עברית" data-language-local-name="Hebrew" class="interlanguage-link-target">עברית</a></li><li class="interlanguage-link interwiki-hu mw-list-item"><a href="https://hu.wikipedia.org/wiki/Deep_learning" title="Deep learning – Hungarian" lang="hu" hreflang="hu" data-title="Deep learning" data-language-autonym="Magyar" data-language-local-name="Hungarian" class="interlanguage-link-target">Magyar</a></li><li class="interlanguage-link interwiki-ml mw-list-item"><a href="https://ml.wikipedia.org/wiki/%E0%B4%A1%E0%B5%80%E0%B4%AA%E0%B5%8D_%E0%B4%B2%E0%B5%87%E0%B4%A3%E0%B4%BF%E0%B4%82%E0%B4%97%E0%B5%8D" title="ഡീപ് ലേണിംഗ് – Malayalam" lang="ml" hreflang="ml" data-title="ഡീപ് ലേണിംഗ്" 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data-mw-ve-target-container> <div class="vector-body-before-content"> <div class="mw-indicators"> </div> <div id="siteSub" class="noprint">From Wikipedia, the free encyclopedia</div> </div> <div id="contentSub"><div id="mw-content-subtitle"></div></div> <div id="mw-content-text" class="mw-body-content"><div class="mw-content-ltr mw-parser-output" lang="en" dir="ltr"><div class="shortdescription nomobile noexcerpt noprint searchaux" style="display:none">Branch of machine learning</div> <style data-mw-deduplicate="TemplateStyles:r1236090951">.mw-parser-output .hatnote{font-style:italic}.mw-parser-output div.hatnote{padding-left:1.6em;margin-bottom:0.5em}.mw-parser-output .hatnote i{font-style:normal}.mw-parser-output .hatnote+link+.hatnote{margin-top:-0.5em}@media print{body.ns-0 .mw-parser-output .hatnote{display:none!important}}</style><div role="note" class="hatnote navigation-not-searchable">For the TV series episode, see <a href="/wiki/Deep_Learning_(South_Park)" title="Deep Learning (South Park)">Deep Learning (South Park)</a>.</div> <figure class="mw-default-size" typeof="mw:File/Thumb"><a href="/wiki/File:Deep_Learning.jpg" class="mw-file-description"><img alt="Representing images on multiple layers of abstraction in deep learning" src="//upload.wikimedia.org/wikipedia/commons/thumb/2/26/Deep_Learning.jpg/300px-Deep_Learning.jpg" decoding="async" width="300" height="245" class="mw-file-element" srcset="//upload.wikimedia.org/wikipedia/commons/thumb/2/26/Deep_Learning.jpg/450px-Deep_Learning.jpg 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/2/26/Deep_Learning.jpg/600px-Deep_Learning.jpg 2x" data-file-width="1239" data-file-height="1012" /></a><figcaption>Representing images on multiple layers of abstraction in deep learning<a href="#cite_note-1">[1]</a></figcaption></figure> <style data-mw-deduplicate="TemplateStyles:r1129693374">.mw-parser-output .hlist dl,.mw-parser-output .hlist ol,.mw-parser-output .hlist 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.sidebar-title-with-pretitle{background:transparent!important}html.skin-theme-clientpref-night .mw-parser-output .sidebar:not(.notheme) .sidebar-title-with-pretitle a{color:var(--color-progressive)!important}}@media screen and (prefers-color-scheme:dark){html.skin-theme-clientpref-os .mw-parser-output .sidebar:not(.notheme) .sidebar-list-title,html.skin-theme-clientpref-os .mw-parser-output .sidebar:not(.notheme) .sidebar-title-with-pretitle{background:transparent!important}html.skin-theme-clientpref-os .mw-parser-output .sidebar:not(.notheme) .sidebar-title-with-pretitle a{color:var(--color-progressive)!important}}@media print{body.ns-0 .mw-parser-output .sidebar{display:none!important}}</style><table class="sidebar sidebar-collapse nomobile nowraplinks hlist"><tbody><tr><td class="sidebar-pretitle">Part of a series on</td></tr><tr><th class="sidebar-title-with-pretitle"><a href="/wiki/Artificial_intelligence" title="Artificial intelligence">Artificial intelligence (AI)</a></th></tr><tr><td class="sidebar-image"><figure class="mw-halign-center" typeof="mw:File"><a href="/wiki/File:Dall-e_3_(jan_%2724)_artificial_intelligence_icon.png" class="mw-file-description"><img src="//upload.wikimedia.org/wikipedia/commons/thumb/6/64/Dall-e_3_%28jan_%2724%29_artificial_intelligence_icon.png/120px-Dall-e_3_%28jan_%2724%29_artificial_intelligence_icon.png" decoding="async" width="100" height="100" class="mw-file-element" srcset="//upload.wikimedia.org/wikipedia/commons/thumb/6/64/Dall-e_3_%28jan_%2724%29_artificial_intelligence_icon.png/250px-Dall-e_3_%28jan_%2724%29_artificial_intelligence_icon.png 1.5x" data-file-width="820" data-file-height="820" /></a><figcaption></figcaption></figure></td></tr><tr><td class="sidebar-content"> <div class="sidebar-list mw-collapsible mw-collapsed"><div class="sidebar-list-title" style="text-align:center;color: var(--color-base)"><a href="/wiki/Artificial_intelligence#Goals" title="Artificial intelligence">Major goals</a></div><div class="sidebar-list-content mw-collapsible-content"> <ul><li><a href="/wiki/Artificial_general_intelligence" title="Artificial general intelligence">Artificial general intelligence</a></li> <li><a href="/wiki/Intelligent_agent" title="Intelligent agent">Intelligent agent</a></li> <li><a href="/wiki/Recursive_self-improvement" title="Recursive self-improvement">Recursive self-improvement</a></li> <li><a href="/wiki/Automated_planning_and_scheduling" title="Automated planning and scheduling">Planning</a></li> <li><a href="/wiki/Computer_vision" title="Computer vision">Computer vision</a></li> <li><a href="/wiki/General_game_playing" title="General game playing">General game playing</a></li> <li><a href="/wiki/Knowledge_representation_and_reasoning" title="Knowledge representation and reasoning">Knowledge reasoning</a></li> <li><a href="/wiki/Natural_language_processing" title="Natural language processing">Natural language processing</a></li> <li><a href="/wiki/Robotics" title="Robotics">Robotics</a></li> <li><a href="/wiki/AI_safety" title="AI safety">AI safety</a></li></ul></div></div></td> </tr><tr><td class="sidebar-content"> <div class="sidebar-list mw-collapsible"><div class="sidebar-list-title" style="text-align:center;color: var(--color-base)">Approaches</div><div class="sidebar-list-content mw-collapsible-content"> <ul><li><a href="/wiki/Machine_learning" title="Machine learning">Machine learning</a></li> <li><a href="/wiki/Symbolic_artificial_intelligence" title="Symbolic artificial intelligence">Symbolic</a></li> <li><a class="mw-selflink selflink">Deep learning</a></li> <li><a href="/wiki/Bayesian_network" title="Bayesian network">Bayesian networks</a></li> <li><a href="/wiki/Evolutionary_algorithm" title="Evolutionary algorithm">Evolutionary algorithms</a></li> <li><a href="/wiki/Hybrid_intelligent_system" title="Hybrid intelligent system">Hybrid intelligent systems</a></li> <li><a href="/wiki/Artificial_intelligence_systems_integration" title="Artificial intelligence systems integration">Systems integration</a></li></ul></div></div></td> </tr><tr><td class="sidebar-content"> <div class="sidebar-list mw-collapsible mw-collapsed"><div class="sidebar-list-title" style="text-align:center;color: var(--color-base)"><a href="/wiki/Applications_of_artificial_intelligence" title="Applications of artificial intelligence">Applications</a></div><div class="sidebar-list-content mw-collapsible-content"> <ul><li><a href="/wiki/Machine_learning_in_bioinformatics" title="Machine learning in bioinformatics">Bioinformatics</a></li> <li><a href="/wiki/Deepfake" title="Deepfake">Deepfake</a></li> <li><a href="/wiki/Machine_learning_in_earth_sciences" title="Machine learning in earth sciences">Earth sciences</a></li> <li><a href="/wiki/Applications_of_artificial_intelligence#Finance" title="Applications of artificial intelligence"> Finance </a></li> <li><a href="/wiki/Generative_artificial_intelligence" title="Generative artificial intelligence">Generative AI</a> <ul><li><a href="/wiki/Artificial_intelligence_art" title="Artificial intelligence art">Art</a></li> <li><a href="/wiki/Generative_audio" title="Generative audio">Audio</a></li> <li><a href="/wiki/Music_and_artificial_intelligence" title="Music and artificial intelligence">Music</a></li></ul></li> <li><a href="/wiki/Artificial_intelligence_in_government" title="Artificial intelligence in government">Government</a></li> <li><a href="/wiki/Artificial_intelligence_in_healthcare" title="Artificial intelligence in healthcare">Healthcare</a> <ul><li><a href="/wiki/Artificial_intelligence_in_mental_health" title="Artificial intelligence in mental health">Mental health</a></li></ul></li> <li><a href="/wiki/Artificial_intelligence_in_industry" title="Artificial intelligence in industry">Industry</a></li> <li><a href="/wiki/Machine_translation" title="Machine translation">Translation</a></li> <li><a href="/wiki/Artificial_intelligence_arms_race" title="Artificial intelligence arms race"> Military </a></li> <li><a href="/wiki/Machine_learning_in_physics" title="Machine learning in physics">Physics</a></li> <li><a href="/wiki/List_of_artificial_intelligence_projects" title="List of artificial intelligence projects">Projects</a></li></ul></div></div></td> </tr><tr><td class="sidebar-content"> <div class="sidebar-list mw-collapsible mw-collapsed"><div class="sidebar-list-title" style="text-align:center;color: var(--color-base)"><a href="/wiki/Philosophy_of_artificial_intelligence" title="Philosophy of artificial intelligence">Philosophy</a></div><div class="sidebar-list-content mw-collapsible-content"> <ul><li><a href="/wiki/Artificial_consciousness" title="Artificial consciousness">Artificial consciousness</a></li> <li><a href="/wiki/Chinese_room" title="Chinese room">Chinese room</a></li> <li><a href="/wiki/Friendly_artificial_intelligence" title="Friendly artificial intelligence">Friendly AI</a></li> <li><a href="/wiki/AI_control_problem" class="mw-redirect" title="AI control problem">Control problem</a>/<a href="/wiki/AI_takeover" title="AI takeover">Takeover</a></li> <li><a href="/wiki/Ethics_of_artificial_intelligence" title="Ethics of artificial intelligence">Ethics</a></li> <li><a href="/wiki/Existential_risk_from_artificial_general_intelligence" class="mw-redirect" title="Existential risk from artificial general intelligence">Existential risk</a></li> <li><a href="/wiki/Turing_test" title="Turing test">Turing test</a></li> <li><a href="/wiki/Uncanny_valley" title="Uncanny valley">Uncanny valley</a></li></ul></div></div></td> </tr><tr><td class="sidebar-content"> <div class="sidebar-list mw-collapsible mw-collapsed"><div class="sidebar-list-title" style="text-align:center;color: var(--color-base)"><a href="/wiki/History_of_artificial_intelligence" title="History of artificial intelligence">History</a></div><div class="sidebar-list-content mw-collapsible-content"> <ul><li><a href="/wiki/Timeline_of_artificial_intelligence" title="Timeline of artificial intelligence">Timeline</a></li> <li><a href="/wiki/Progress_in_artificial_intelligence" title="Progress in artificial intelligence">Progress</a></li> <li><a href="/wiki/AI_winter" title="AI winter">AI winter</a></li> <li><a href="/wiki/AI_boom" title="AI boom">AI boom</a></li></ul></div></div></td> </tr><tr><td class="sidebar-content"> <div class="sidebar-list mw-collapsible mw-collapsed"><div class="sidebar-list-title" style="text-align:center;color: var(--color-base)">Glossary</div><div class="sidebar-list-content mw-collapsible-content"> <ul><li><a href="/wiki/Glossary_of_artificial_intelligence" title="Glossary of artificial intelligence">Glossary</a></li></ul></div></div></td> </tr><tr><td class="sidebar-navbar"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1129693374" /><style data-mw-deduplicate="TemplateStyles:r1239400231">.mw-parser-output .navbar{display:inline;font-size:88%;font-weight:normal}.mw-parser-output .navbar-collapse{float:left;text-align:left}.mw-parser-output .navbar-boxtext{word-spacing:0}.mw-parser-output .navbar ul{display:inline-block;white-space:nowrap;line-height:inherit}.mw-parser-output .navbar-brackets::before{margin-right:-0.125em;content:"[ "}.mw-parser-output .navbar-brackets::after{margin-left:-0.125em;content:" ]"}.mw-parser-output .navbar li{word-spacing:-0.125em}.mw-parser-output .navbar a>span,.mw-parser-output .navbar a>abbr{text-decoration:inherit}.mw-parser-output .navbar-mini abbr{font-variant:small-caps;border-bottom:none;text-decoration:none;cursor:inherit}.mw-parser-output .navbar-ct-full{font-size:114%;margin:0 7em}.mw-parser-output .navbar-ct-mini{font-size:114%;margin:0 4em}html.skin-theme-clientpref-night .mw-parser-output .navbar li a abbr{color:var(--color-base)!important}@media(prefers-color-scheme:dark){html.skin-theme-clientpref-os .mw-parser-output .navbar li a abbr{color:var(--color-base)!important}}@media print{.mw-parser-output .navbar{display:none!important}}</style><div class="navbar plainlinks hlist navbar-mini"><ul><li class="nv-view"><a href="/wiki/Template:Artificial_intelligence" title="Template:Artificial intelligence"><abbr title="View this template">v</abbr></a></li><li class="nv-talk"><a href="/wiki/Template_talk:Artificial_intelligence" title="Template talk:Artificial intelligence"><abbr title="Discuss this template">t</abbr></a></li><li class="nv-edit"><a href="/wiki/Special:EditPage/Template:Artificial_intelligence" title="Special:EditPage/Template:Artificial intelligence"><abbr title="Edit this template">e</abbr></a></li></ul></div></td></tr></tbody></table> Deep learning is a subset of <a href="/wiki/Machine_learning" title="Machine learning">machine learning</a> that focuses on utilizing <a href="/wiki/Neural_network_(machine_learning)" title="Neural network (machine learning)">neural networks</a> to perform tasks such as <a href="/wiki/Statistical_classification" title="Statistical classification">classification</a>, <a href="/wiki/Regression_analysis" title="Regression analysis">regression</a>, and <a href="/wiki/Representation_learning" class="mw-redirect" title="Representation learning">representation learning</a>. The field takes inspiration from <a href="/wiki/Neuroscience" title="Neuroscience">biological neuroscience</a> and is centered around stacking <a href="/wiki/Artificial_neuron" title="Artificial neuron">artificial neurons</a> into layers and "training" them to process data. The adjective "deep" refers to the use of multiple layers (ranging from three to several hundred or thousands) in the network. Methods used can be either <a href="/wiki/Supervised_learning" title="Supervised learning">supervised</a>, <a href="/wiki/Semi-supervised_learning" class="mw-redirect" title="Semi-supervised learning">semi-supervised</a> or <a href="/wiki/Unsupervised_learning" title="Unsupervised learning">unsupervised</a>.<a href="#cite_note-NatureBengio-2">[2]</a> Some common deep learning network architectures include <a href="/wiki/Fully_connected_network" class="mw-redirect" title="Fully connected network">fully connected networks</a>, <a href="/wiki/Deep_belief_network" title="Deep belief network">deep belief networks</a>, <a href="/wiki/Recurrent_neural_networks" class="mw-redirect" title="Recurrent neural networks">recurrent neural networks</a>, <a href="/wiki/Convolutional_neural_networks" class="mw-redirect" title="Convolutional neural networks">convolutional neural networks</a>, <a href="/wiki/Generative_adversarial_network" title="Generative adversarial network">generative adversarial networks</a>, <a href="/wiki/Transformer_(machine_learning_model)" class="mw-redirect" title="Transformer (machine learning model)">transformers</a>, and <a href="/wiki/Neural_radiance_field" title="Neural radiance field">neural radiance fields</a>. These architectures have been applied to fields including <a href="/wiki/Computer_vision" title="Computer vision">computer vision</a>, <a href="/wiki/Speech_recognition" title="Speech recognition">speech recognition</a>, <a href="/wiki/Natural_language_processing" title="Natural language processing">natural language processing</a>, <a href="/wiki/Machine_translation" title="Machine translation">machine translation</a>, <a href="/wiki/Bioinformatics" title="Bioinformatics">bioinformatics</a>, <a href="/wiki/Drug_design" title="Drug design">drug design</a>, <a href="/wiki/Medical_image_analysis" class="mw-redirect" title="Medical image analysis">medical image analysis</a>, <a href="/wiki/Climatology" title="Climatology">climate science</a>, material inspection and <a href="/wiki/Board_game" title="Board game">board game</a> programs, where they have produced results comparable to and in some cases surpassing human expert performance.<a href="#cite_note-:9-3">[3]</a><a href="#cite_note-krizhevsky2012-4">[4]</a><a href="#cite_note-5">[5]</a> Early forms of neural networks were inspired by information processing and distributed communication nodes in <a href="/wiki/Biological_system" title="Biological system">biological systems</a>, particularly the <a href="/wiki/Human_brain" title="Human brain">human brain</a>. However, current neural networks do not intend to model the brain function of organisms, and are generally seen as low-quality models for that purpose.<a href="#cite_note-6">[6]</a> <style data-mw-deduplicate="TemplateStyles:r886046785">.mw-parser-output .toclimit-2 .toclevel-1 ul,.mw-parser-output .toclimit-3 .toclevel-2 ul,.mw-parser-output .toclimit-4 .toclevel-3 ul,.mw-parser-output .toclimit-5 .toclevel-4 ul,.mw-parser-output .toclimit-6 .toclevel-5 ul,.mw-parser-output .toclimit-7 .toclevel-6 ul{display:none}</style><div class="toclimit-3"><meta property="mw:PageProp/toc" /></div> <div class="mw-heading mw-heading2"><h2 id="Overview">Overview</h2>[<a href="/w/index.php?title=Deep_learning&action=edit&section=1" title="Edit section: Overview">edit</a>]</div> Most modern deep learning models are based on multi-layered <a href="/wiki/Neural_network_(machine_learning)" title="Neural network (machine learning)">neural networks</a> such as <a href="/wiki/Convolutional_neural_network" title="Convolutional neural network">convolutional neural networks</a> and <a href="/wiki/Transformer_(neural_network)" class="mw-redirect" title="Transformer (neural network)">transformers</a>, although they can also include <a href="/wiki/Propositional_formula" title="Propositional formula">propositional formulas</a> or latent variables organized layer-wise in deep <a href="/wiki/Generative_model" title="Generative model">generative models</a> such as the nodes in <a href="/wiki/Deep_belief_network" title="Deep belief network">deep belief networks</a> and deep <a href="/wiki/Boltzmann_machine" title="Boltzmann machine">Boltzmann machines</a>.<a href="#cite_note-BENGIODEEP-7">[7]</a> Fundamentally, deep learning refers to a class of <a href="/wiki/Machine_learning" title="Machine learning">machine learning</a> <a href="/wiki/Algorithm" title="Algorithm">algorithms</a> in which a hierarchy of layers is used to transform input data into a progressively more abstract and composite representation. For example, in an <a href="/wiki/Image_recognition" class="mw-redirect" title="Image recognition">image recognition</a> model, the raw input may be an <a href="/wiki/Image" title="Image">image</a> (represented as a <a href="/wiki/Tensor_(machine_learning)" title="Tensor (machine learning)">tensor</a> of <a href="/wiki/Pixel" title="Pixel">pixels</a>). The first representational layer may attempt to identify basic shapes such as lines and circles, the second layer may compose and encode arrangements of edges, the third layer may encode a nose and eyes, and the fourth layer may recognize that the image contains a face. Importantly, a deep learning process can learn which features to optimally place at which level on its own. Prior to deep learning, machine learning techniques often involved hand-crafted <a href="/wiki/Feature_engineering" title="Feature engineering">feature engineering</a> to transform the data into a more suitable representation for a classification algorithm to operate on. In the deep learning approach, features are not hand-crafted and the model <a href="/wiki/Representation_learning" class="mw-redirect" title="Representation learning">discovers</a> useful feature representations from the data automatically. This does not eliminate the need for hand-tuning; for example, varying numbers of layers and layer sizes can provide different degrees of abstraction.<a href="#cite_note-BENGIO2012-8">[8]</a><a href="#cite_note-NatureBengio-2">[2]</a> The word "deep" in "deep learning" refers to the number of layers through which the data is transformed. More precisely, deep learning systems have a substantial credit assignment path (CAP) depth. The CAP is the chain of transformations from input to output. CAPs describe potentially causal connections between input and output. For a <a href="/wiki/Feedforward_neural_network" title="Feedforward neural network">feedforward neural network</a>, the depth of the CAPs is that of the network and is the number of hidden layers plus one (as the output layer is also parameterized). For <a href="/wiki/Recurrent_neural_network" title="Recurrent neural network">recurrent neural networks</a>, in which a signal may propagate through a layer more than once, the CAP depth is potentially unlimited.<a href="#cite_note-SCHIDHUB-9">[9]</a> No universally agreed-upon threshold of depth divides shallow learning from deep learning, but most researchers agree that deep learning involves CAP depth higher than two. CAP of depth two has been shown to be a universal approximator in the sense that it can emulate any function.<a href="#cite_note-10">[10]</a> Beyond that, more layers do not add to the function approximator ability of the network. Deep models (CAP > two) are able to extract better features than shallow models and hence, extra layers help in learning the features effectively. Deep learning architectures can be constructed with a <a href="/wiki/Greedy_algorithm" title="Greedy algorithm">greedy</a> layer-by-layer method.<a href="#cite_note-BENGIO2007-11">[11]</a> Deep learning helps to disentangle these abstractions and pick out which features improve performance.<a href="#cite_note-BENGIO2012-8">[8]</a> Deep learning algorithms can be applied to unsupervised learning tasks. This is an important benefit because unlabeled data are more abundant than the labeled data. Examples of deep structures that can be trained in an unsupervised manner are <a href="/wiki/Deep_belief_network" title="Deep belief network">deep belief networks</a>.<a href="#cite_note-BENGIO2012-8">[8]</a><a href="#cite_note-SCHOLARDBNS-12">[12]</a> The term Deep Learning was introduced to the machine learning community by <a href="/wiki/Rina_Dechter" title="Rina Dechter">Rina Dechter</a> in 1986,<a href="#cite_note-dechter1986-13">[13]</a> and to artificial neural networks by Igor Aizenberg and colleagues in 2000, in the context of <a href="/wiki/Boolean_network" title="Boolean network">Boolean</a> threshold neurons.<a href="#cite_note-MV_1-14">[14]</a><a href="#cite_note-15">[15]</a> Although the history of its appearance is apparently more complicated.<a href="#cite_note-16">[16]</a> <div class="mw-heading mw-heading2"><h2 id="Interpretations">Interpretations</h2>[<a href="/w/index.php?title=Deep_learning&action=edit&section=2" title="Edit section: Interpretations">edit</a>]</div> Deep neural networks are generally interpreted in terms of the <a href="/wiki/Universal_approximation_theorem" title="Universal approximation theorem">universal approximation theorem</a><a href="#cite_note-cyb-17">[17]</a><a href="#cite_note-horn-18">[18]</a><a href="#cite_note-Haykin,_Simon_1998-19">[19]</a><a href="#cite_note-Hassoun,_M._1995_p._48-20">[20]</a><a href="#cite_note-ZhouLu-21">[21]</a> or <a href="/wiki/Bayesian_inference" title="Bayesian inference">probabilistic inference</a>.<a href="#cite_note-22">[22]</a><a href="#cite_note-BOOK2014-23">[23]</a><a href="#cite_note-BENGIO2012-8">[8]</a><a href="#cite_note-SCHIDHUB-9">[9]</a><a href="#cite_note-MURPHY-24">[24]</a> The classic universal approximation theorem concerns the capacity of <a href="/wiki/Feedforward_neural_networks" class="mw-redirect" title="Feedforward neural networks">feedforward neural networks</a> with a single hidden layer of finite size to approximate <a href="/wiki/Continuous_functions" class="mw-redirect" title="Continuous functions">continuous functions</a>.<a href="#cite_note-cyb-17">[17]</a><a href="#cite_note-horn-18">[18]</a><a href="#cite_note-Haykin,_Simon_1998-19">[19]</a><a href="#cite_note-Hassoun,_M._1995_p._48-20">[20]</a> In 1989, the first proof was published by <a href="/wiki/George_Cybenko" title="George Cybenko">George Cybenko</a> for <a href="/wiki/Sigmoid_function" title="Sigmoid function">sigmoid</a> activation functions<a href="#cite_note-cyb-17">[17]</a> and was generalised to feed-forward multi-layer architectures in 1991 by Kurt Hornik.<a href="#cite_note-horn-18">[18]</a> Recent work also showed that universal approximation also holds for non-bounded activation functions such as <a href="/wiki/Kunihiko_Fukushima" title="Kunihiko Fukushima">Kunihiko Fukushima</a>'s <a href="/wiki/Rectified_linear_unit" class="mw-redirect" title="Rectified linear unit">rectified linear unit</a>.<a href="#cite_note-Fukushima1969-25">[25]</a><a href="#cite_note-sonoda17-26">[26]</a> The universal approximation theorem for <a href="/wiki/Deep_neural_network" class="mw-redirect" title="Deep neural network">deep neural networks</a> concerns the capacity of networks with bounded width but the depth is allowed to grow. Lu et al.<a href="#cite_note-ZhouLu-21">[21]</a> proved that if the width of a deep neural network with <a href="/wiki/ReLU" class="mw-redirect" title="ReLU">ReLU</a> activation is strictly larger than the input dimension, then the network can approximate any <a href="/wiki/Lebesgue_integration" class="mw-redirect" title="Lebesgue integration">Lebesgue integrable function</a>; if the width is smaller or equal to the input dimension, then a deep neural network is not a universal approximator. The <a href="/wiki/Probabilistic" class="mw-redirect" title="Probabilistic">probabilistic</a> interpretation<a href="#cite_note-MURPHY-24">[24]</a> derives from the field of <a href="/wiki/Machine_learning" title="Machine learning">machine learning</a>. It features inference,<a href="#cite_note-BOOK2014-23">[23]</a><a href="#cite_note-BENGIODEEP-7">[7]</a><a href="#cite_note-BENGIO2012-8">[8]</a><a href="#cite_note-SCHIDHUB-9">[9]</a><a href="#cite_note-SCHOLARDBNS-12">[12]</a><a href="#cite_note-MURPHY-24">[24]</a> as well as the <a href="/wiki/Optimization" class="mw-redirect" title="Optimization">optimization</a> concepts of <a href="/wiki/Training" title="Training">training</a> and <a href="/wiki/Test_(assessment)" class="mw-redirect" title="Test (assessment)">testing</a>, related to fitting and <a href="/wiki/Generalization" title="Generalization">generalization</a>, respectively. More specifically, the probabilistic interpretation considers the activation nonlinearity as a <a href="/wiki/Cumulative_distribution_function" title="Cumulative distribution function">cumulative distribution function</a>.<a href="#cite_note-MURPHY-24">[24]</a> The probabilistic interpretation led to the introduction of <a href="/wiki/Dropout_(neural_networks)" class="mw-redirect" title="Dropout (neural networks)">dropout</a> as <a href="/wiki/Regularization_(mathematics)" title="Regularization (mathematics)">regularizer</a> in neural networks. The probabilistic interpretation was introduced by researchers including <a href="/wiki/John_Hopfield" title="John Hopfield">Hopfield</a>, <a href="/wiki/Bernard_Widrow" title="Bernard Widrow">Widrow</a> and <a href="/wiki/Kumpati_S._Narendra" title="Kumpati S. Narendra">Narendra</a> and popularized in surveys such as the one by <a href="/wiki/Christopher_Bishop" title="Christopher Bishop">Bishop</a>.<a href="#cite_note-prml-27">[27]</a> <div class="mw-heading mw-heading2"><h2 id="History">History</h2>[<a href="/w/index.php?title=Deep_learning&action=edit&section=3" title="Edit section: History">edit</a>]</div> <div class="mw-heading mw-heading3"><h3 id="Before_1980">Before 1980</h3>[<a href="/w/index.php?title=Deep_learning&action=edit&section=4" title="Edit section: Before 1980">edit</a>]</div> There are two <a href="/wiki/Types_of_artificial_neural_networks" title="Types of artificial neural networks">types</a> of artificial neural network (ANN): <a href="/wiki/Feedforward_neural_network" title="Feedforward neural network">feedforward neural network</a> (FNN) or <a href="/wiki/Multilayer_perceptron" title="Multilayer perceptron">multilayer perceptron</a> (MLP) and <a href="/wiki/Recurrent_neural_networks" class="mw-redirect" title="Recurrent neural networks">recurrent neural networks</a> (RNN). RNNs have cycles in their connectivity structure, FNNs don't. In the 1920s, <a href="/wiki/Wilhelm_Lenz" title="Wilhelm Lenz">Wilhelm Lenz</a> and <a href="/wiki/Ernst_Ising" title="Ernst Ising">Ernst Ising</a> created the <a href="/wiki/Ising_model" title="Ising model">Ising model</a><a href="#cite_note-ising1925-28">[28]</a><a href="#cite_note-brush67-29">[29]</a> which is essentially a non-learning RNN architecture consisting of neuron-like threshold elements. In 1972, <a href="/wiki/Shun%27ichi_Amari" title="Shun'ichi Amari">Shun'ichi Amari</a> made this architecture adaptive.<a href="#cite_note-Amari1972-30">[30]</a><a href="#cite_note-DLhistory-31">[31]</a> His learning RNN was republished by <a href="/wiki/John_Hopfield" title="John Hopfield">John Hopfield</a> in 1982.<a href="#cite_note-Hopfield1982-32">[32]</a> Other early <a href="/wiki/Recurrent_neural_network" title="Recurrent neural network">recurrent neural networks</a> were published by Kaoru Nakano in 1971.<a href="#cite_note-Nakano1971-33">[33]</a><a href="#cite_note-Nakano1972-34">[34]</a> Already in 1948, <a href="/wiki/Alan_Turing" title="Alan Turing">Alan Turing</a> produced work on "Intelligent Machinery" that was not published in his lifetime,<a href="#cite_note-turing1948-35">[35]</a> containing "ideas related to artificial evolution and learning RNNs".<a href="#cite_note-DLhistory-31">[31]</a> <a href="/wiki/Frank_Rosenblatt" title="Frank Rosenblatt">Frank Rosenblatt</a> (1958)<a href="#cite_note-36">[36]</a> proposed the perceptron, an MLP with 3 layers: an input layer, a hidden layer with randomized weights that did not learn, and an output layer. He later published a 1962 book that also introduced variants and computer experiments, including a version with four-layer perceptrons "with adaptive preterminal networks" where the last two layers have learned weights (here he credits H. D. Block and B. W. Knight).<a href="#cite_note-rosenblatt1962-37">[37]</a>: section 16  The book cites an earlier network by R. D. Joseph (1960)<a href="#cite_note-joseph1960-38">[38]</a> "functionally equivalent to a variation of" this four-layer system (the book mentions Joseph over 30 times). Should Joseph therefore be considered the originator of proper adaptive <a href="/wiki/Multilayer_perceptrons" class="mw-redirect" title="Multilayer perceptrons">multilayer perceptrons</a> with learning hidden units? Unfortunately, the learning algorithm was not a functional one, and fell into oblivion. The first working deep learning algorithm was the <a href="/wiki/Group_method_of_data_handling" title="Group method of data handling">Group method of data handling</a>, a method to train arbitrarily deep neural networks, published by <a href="/wiki/Alexey_Ivakhnenko" title="Alexey Ivakhnenko">Alexey Ivakhnenko</a> and Lapa in 1965. They regarded it as a form of polynomial regression,<a href="#cite_note-ivak1965-39">[39]</a> or a generalization of Rosenblatt's perceptron.<a href="#cite_note-40">[40]</a> A 1971 paper described a deep network with eight layers trained by this method,<a href="#cite_note-ivak1971-41">[41]</a> which is based on layer by layer training through regression analysis. Superfluous hidden units are pruned using a separate validation set. Since the activation functions of the nodes are Kolmogorov-Gabor polynomials, these were also the first deep networks with multiplicative units or "gates".<a href="#cite_note-DLhistory-31">[31]</a> The first deep learning <a href="/wiki/Multilayer_perceptron" title="Multilayer perceptron">multilayer perceptron</a> trained by <a href="/wiki/Stochastic_gradient_descent" title="Stochastic gradient descent">stochastic gradient descent</a><a href="#cite_note-robbins1951-42">[42]</a> was published in 1967 by <a href="/wiki/Shun%27ichi_Amari" title="Shun'ichi Amari">Shun'ichi Amari</a>.<a href="#cite_note-Amari1967-43">[43]</a> In computer experiments conducted by Amari's student Saito, a five layer MLP with two modifiable layers learned <a href="/wiki/Knowledge_representation" class="mw-redirect" title="Knowledge representation">internal representations</a> to classify non-linearily separable pattern classes.<a href="#cite_note-DLhistory-31">[31]</a> Subsequent developments in hardware and hyperparameter tunings have made end-to-end <a href="/wiki/Stochastic_gradient_descent" title="Stochastic gradient descent">stochastic gradient descent</a> the currently dominant training technique. In 1969, <a href="/wiki/Kunihiko_Fukushima" title="Kunihiko Fukushima">Kunihiko Fukushima</a> introduced the <a href="/wiki/Rectifier_(neural_networks)" title="Rectifier (neural networks)">ReLU</a> (rectified linear unit) <a href="/wiki/Activation_function" title="Activation function">activation function</a>.<a href="#cite_note-Fukushima1969-25">[25]</a><a href="#cite_note-DLhistory-31">[31]</a> The rectifier has become the most popular activation function for deep learning.<a href="#cite_note-44">[44]</a> Deep learning architectures for <a href="/wiki/Convolutional_neural_network" title="Convolutional neural network">convolutional neural networks</a> (CNNs) with convolutional layers and downsampling layers began with the <a href="/wiki/Neocognitron" title="Neocognitron">Neocognitron</a> introduced by <a href="/wiki/Kunihiko_Fukushima" title="Kunihiko Fukushima">Kunihiko Fukushima</a> in 1979, though not trained by backpropagation.<a href="#cite_note-FUKU1979-45">[45]</a><a href="#cite_note-FUKU1980-46">[46]</a> <a href="/wiki/Backpropagation" title="Backpropagation">Backpropagation</a> is an efficient application of the <a href="/wiki/Chain_rule" title="Chain rule">chain rule</a> derived by <a href="/wiki/Gottfried_Wilhelm_Leibniz" title="Gottfried Wilhelm Leibniz">Gottfried Wilhelm Leibniz</a> in 1673<a href="#cite_note-leibniz1676-47">[47]</a> to networks of differentiable nodes. The terminology "back-propagating errors" was actually introduced in 1962 by Rosenblatt,<a href="#cite_note-rosenblatt1962-37">[37]</a> but he did not know how to implement this, although <a href="/wiki/Henry_J._Kelley" title="Henry J. Kelley">Henry J. Kelley</a> had a continuous precursor of backpropagation in 1960 in the context of <a href="/wiki/Control_theory" title="Control theory">control theory</a>.<a href="#cite_note-kelley1960-48">[48]</a> The modern form of backpropagation was first published in <a href="/wiki/Seppo_Linnainmaa" title="Seppo Linnainmaa">Seppo Linnainmaa</a>'s master thesis (1970).<a href="#cite_note-lin19703-49">[49]</a><a href="#cite_note-lin19763-50">[50]</a><a href="#cite_note-DLhistory-31">[31]</a> G.M. Ostrovski et al. republished it in 1971.<a href="#cite_note-ostrowski1971-51">[51]</a><a href="#cite_note-backprop-52">[52]</a> <a href="/wiki/Paul_Werbos" title="Paul Werbos">Paul Werbos</a> applied backpropagation to neural networks in 1982<a href="#cite_note-werbos1982-53">[53]</a> (his 1974 PhD thesis, reprinted in a 1994 book,<a href="#cite_note-werbos1974-54">[54]</a> did not yet describe the algorithm<a href="#cite_note-backprop-52">[52]</a>). In 1986, <a href="/wiki/David_E._Rumelhart" class="mw-redirect" title="David E. Rumelhart">David E. Rumelhart</a> et al. popularised backpropagation but did not cite the original work.<a href="#cite_note-55">[55]</a><a href="#cite_note-rumelhart1986-56">[56]</a> <div class="mw-heading mw-heading3"><h3 id="1980s-2000s">1980s-2000s</h3>[<a href="/w/index.php?title=Deep_learning&action=edit&section=5" title="Edit section: 1980s-2000s">edit</a>]</div> The <a href="/wiki/Time_delay_neural_network" title="Time delay neural network">time delay neural network</a> (TDNN) was introduced in 1987 by <a href="/wiki/Alex_Waibel" title="Alex Waibel">Alex Waibel</a> to apply CNN to phoneme recognition. It used convolutions, weight sharing, and backpropagation.<a href="#cite_note-Waibel1987-57">[57]</a><a href="#cite_note-speechsignal-58">[58]</a> In 1988, Wei Zhang applied a backpropagation-trained CNN to alphabet recognition.<a href="#cite_note-wz1988-59">[59]</a> In 1989, <a href="/wiki/Yann_LeCun" title="Yann LeCun">Yann LeCun</a> et al. created a CNN called <a href="/wiki/LeNet" title="LeNet">LeNet</a> for <a href="/wiki/Handwriting_recognition" title="Handwriting recognition">recognizing handwritten ZIP codes</a> on mail. Training required 3 days.<a href="#cite_note-LECUN1989-60">[60]</a> In 1990, Wei Zhang implemented a CNN on <a href="/wiki/Optical_computing" title="Optical computing">optical computing</a> hardware.<a href="#cite_note-wz1990-61">[61]</a> In 1991, a CNN was applied to medical image object segmentation<a href="#cite_note-62">[62]</a> and breast cancer detection in mammograms.<a href="#cite_note-63">[63]</a> <a href="/wiki/LeNet" title="LeNet">LeNet</a>-5 (1998), a 7-level CNN by <a href="/wiki/Yann_LeCun" title="Yann LeCun">Yann LeCun</a> et al., that classifies digits, was applied by several banks to recognize hand-written numbers on checks digitized in 32x32 pixel images.<a href="#cite_note-lecun98-64">[64]</a> <a href="/wiki/Recurrent_neural_network" title="Recurrent neural network">Recurrent neural networks</a> (RNN)<a href="#cite_note-ising1925-28">[28]</a><a href="#cite_note-Amari1972-30">[30]</a> were further developed in the 1980s. Recurrence is used for sequence processing, and when a recurrent network is unrolled, it mathematically resembles a deep feedforward layer. Consequently, they have similar properties and issues, and their developments had mutual influences. In RNN, two early influential works were the <a href="/wiki/Recurrent_neural_network#Jordan_network" title="Recurrent neural network">Jordan network</a> (1986)<a href="#cite_note-65">[65]</a> and the <a href="/wiki/Recurrent_neural_network#Elman_network" title="Recurrent neural network">Elman network</a> (1990),<a href="#cite_note-66">[66]</a> which applied RNN to study problems in <a href="/wiki/Cognitive_psychology" title="Cognitive psychology">cognitive psychology</a>. In the 1980s, backpropagation did not work well for deep learning with long credit assignment paths. To overcome this problem, in 1991, <a href="/wiki/J%C3%BCrgen_Schmidhuber" title="Jürgen Schmidhuber">Jürgen Schmidhuber</a> proposed a hierarchy of RNNs pre-trained one level at a time by <a href="/wiki/Self-supervised_learning" title="Self-supervised learning">self-supervised learning</a> where each RNN tries to predict its own next input, which is the next unexpected input of the RNN below.<a href="#cite_note-chunker1991-67">[67]</a><a href="#cite_note-schmidhuber1992-68">[68]</a> This "neural history compressor" uses <a href="/wiki/Predictive_coding" title="Predictive coding">predictive coding</a> to learn <a href="/wiki/Knowledge_representation" class="mw-redirect" title="Knowledge representation">internal representations</a> at multiple self-organizing time scales. This can substantially facilitate downstream deep learning. The RNN hierarchy can be collapsed into a single RNN, by <a href="/wiki/Knowledge_distillation" title="Knowledge distillation">distilling</a> a higher level chunker network into a lower level automatizer network.<a href="#cite_note-chunker1991-67">[67]</a><a href="#cite_note-schmidhuber1992-68">[68]</a><a href="#cite_note-DLhistory-31">[31]</a> In 1993, a neural history compressor solved a "Very Deep Learning" task that required more than 1000 subsequent <a href="/wiki/Layer_(deep_learning)" title="Layer (deep learning)">layers</a> in an RNN unfolded in time.<a href="#cite_note-schmidhuber1993-69">[69]</a> The "P" in <a href="/wiki/ChatGPT" title="ChatGPT">ChatGPT</a> refers to such pre-training. <a href="/wiki/Sepp_Hochreiter" title="Sepp Hochreiter">Sepp Hochreiter</a>'s diploma thesis (1991)<a href="#cite_note-HOCH1991-70">[70]</a> implemented the neural history compressor,<a href="#cite_note-chunker1991-67">[67]</a> and identified and analyzed the <a href="/wiki/Vanishing_gradient_problem" title="Vanishing gradient problem">vanishing gradient problem</a>.<a href="#cite_note-HOCH1991-70">[70]</a><a href="#cite_note-HOCH2001-71">[71]</a> Hochreiter proposed recurrent <a href="/wiki/Residual_neural_network" title="Residual neural network">residual</a> connections to solve the vanishing gradient problem. This led to the <a href="/wiki/Long_short-term_memory" title="Long short-term memory">long short-term memory</a> (LSTM), published in 1995.<a href="#cite_note-72">[72]</a> LSTM can learn "very deep learning" tasks<a href="#cite_note-SCHIDHUB-9">[9]</a> with long credit assignment paths that require memories of events that happened thousands of discrete time steps before. That LSTM was not yet the modern architecture, which required a "forget gate", introduced in 1999,<a href="#cite_note-lstm1999-73">[73]</a> which became the standard RNN architecture. In 1991, <a href="/wiki/J%C3%BCrgen_Schmidhuber" title="Jürgen Schmidhuber">Jürgen Schmidhuber</a> also published adversarial neural networks that contest with each other in the form of a <a href="/wiki/Zero-sum_game" title="Zero-sum game">zero-sum game</a>, where one network's gain is the other network's loss.<a href="#cite_note-curiosity1991-74">[74]</a><a href="#cite_note-fun2010-75">[75]</a> The first network is a <a href="/wiki/Generative_model" title="Generative model">generative model</a> that models a <a href="/wiki/Probability_distribution" title="Probability distribution">probability distribution</a> over output patterns. The second network learns by <a href="/wiki/Gradient_descent" title="Gradient descent">gradient descent</a> to predict the reactions of the environment to these patterns. This was called "artificial curiosity". In 2014, this principle was used in <a href="/wiki/Generative_adversarial_network" title="Generative adversarial network">generative adversarial networks</a> (GANs).<a href="#cite_note-gancurpm2020-76">[76]</a> During 1985–1995, inspired by statistical mechanics, several architectures and methods were developed by <a href="/wiki/Terry_Sejnowski" title="Terry Sejnowski">Terry Sejnowski</a>, <a href="/wiki/Peter_Dayan" title="Peter Dayan">Peter Dayan</a>, <a href="/wiki/Geoffrey_Hinton" title="Geoffrey Hinton">Geoffrey Hinton</a>, etc., including the <a href="/wiki/Boltzmann_machine" title="Boltzmann machine">Boltzmann machine</a>,<a href="#cite_note-77">[77]</a> <a href="/wiki/Restricted_Boltzmann_machine" title="Restricted Boltzmann machine">restricted Boltzmann machine</a>,<a href="#cite_note-78">[78]</a> <a href="/wiki/Helmholtz_machine" title="Helmholtz machine">Helmholtz machine</a>,<a href="#cite_note-nc95-79">[79]</a> and the <a href="/wiki/Wake-sleep_algorithm" title="Wake-sleep algorithm">wake-sleep algorithm</a>.<a href="#cite_note-:1-80">[80]</a> These were designed for unsupervised learning of deep generative models. However, those were more computationally expensive compared to backpropagation. Boltzmann machine learning algorithm, published in 1985, was briefly popular before being eclipsed by the backpropagation algorithm in 1986. (p. 112 <a href="#cite_note-81">[81]</a>). A 1988 network became state of the art in <a href="/wiki/Protein_structure_prediction" title="Protein structure prediction">protein structure prediction</a>, an early application of deep learning to bioinformatics.<a href="#cite_note-82">[82]</a> Both shallow and deep learning (e.g., recurrent nets) of ANNs for <a href="/wiki/Speech_recognition" title="Speech recognition">speech recognition</a> have been explored for many years.<a href="#cite_note-83">[83]</a><a href="#cite_note-Robinson1992-84">[84]</a><a href="#cite_note-85">[85]</a> These methods never outperformed non-uniform internal-handcrafting Gaussian <a href="/wiki/Mixture_model" title="Mixture model">mixture model</a>/<a href="/wiki/Hidden_Markov_model" title="Hidden Markov model">Hidden Markov model</a> (GMM-HMM) technology based on generative models of speech trained discriminatively.<a href="#cite_note-Baker2009-86">[86]</a> Key difficulties have been analyzed, including gradient diminishing<a href="#cite_note-HOCH1991-70">[70]</a> and weak temporal correlation structure in neural predictive models.<a href="#cite_note-Bengio1991-87">[87]</a><a href="#cite_note-Deng1994-88">[88]</a> Additional difficulties were the lack of training data and limited computing power. Most <a href="/wiki/Speech_recognition" title="Speech recognition">speech recognition</a> researchers moved away from neural nets to pursue generative modeling. An exception was at <a href="/wiki/SRI_International" title="SRI International">SRI International</a> in the late 1990s. Funded by the US government's <a href="/wiki/National_Security_Agency" title="National Security Agency">NSA</a> and <a href="/wiki/DARPA" title="DARPA">DARPA</a>, SRI researched in speech and <a href="/wiki/Speaker_recognition" title="Speaker recognition">speaker recognition</a>. The speaker recognition team led by <a href="/wiki/Larry_Heck" title="Larry Heck">Larry Heck</a> reported significant success with deep neural networks in speech processing in the 1998 <a href="/wiki/National_Institute_of_Standards_and_Technology" title="National Institute of Standards and Technology">NIST</a> Speaker Recognition benchmark.<a href="#cite_note-Doddington2000-89">[89]</a><a href="#cite_note-Heck2000-90">[90]</a> It was deployed in the Nuance Verifier, representing the first major industrial application of deep learning.<a href="#cite_note-91">[91]</a> The principle of elevating "raw" features over hand-crafted optimization was first explored successfully in the architecture of deep autoencoder on the "raw" spectrogram or linear <a href="/wiki/Filter_bank" title="Filter bank">filter-bank</a> features in the late 1990s,<a href="#cite_note-Heck2000-90">[90]</a> showing its superiority over the <a href="/wiki/Mel-frequency_cepstrum" title="Mel-frequency cepstrum">Mel-Cepstral</a> features that contain stages of fixed transformation from spectrograms. The raw features of speech, <a href="/wiki/Waveform" title="Waveform">waveforms</a>, later produced excellent larger-scale results.<a href="#cite_note-92">[92]</a> <div class="mw-heading mw-heading3"><h3 id="2000s">2000s</h3>[<a href="/w/index.php?title=Deep_learning&action=edit&section=6" title="Edit section: 2000s">edit</a>]</div> Neural networks entered a null, and simpler models that use task-specific handcrafted features such as <a href="/wiki/Gabor_filter" title="Gabor filter">Gabor filters</a> and <a href="/wiki/Support_vector_machine" title="Support vector machine">support vector machines</a> (SVMs) became the preferred choices in the 1990s and 2000s, because of artificial neural networks' computational cost and a lack of understanding of how the brain wires its biological networks.[<a href="/wiki/Wikipedia:Citation_needed" title="Wikipedia:Citation needed">citation needed</a>] In 2003, LSTM became competitive with traditional speech recognizers on certain tasks.<a href="#cite_note-graves2003-93">[93]</a> In 2006, <a href="/wiki/Alex_Graves_(computer_scientist)" title="Alex Graves (computer scientist)">Alex Graves</a>, Santiago Fernández, Faustino Gomez, and Schmidhuber combined it with <a href="/wiki/Connectionist_temporal_classification" title="Connectionist temporal classification">connectionist temporal classification</a> (CTC)<a href="#cite_note-graves2006-94">[94]</a> in stacks of LSTMs.<a href="#cite_note-fernandez2007keyword-95">[95]</a> In 2009, it became the first RNN to win a <a href="/wiki/Pattern_recognition" title="Pattern recognition">pattern recognition</a> contest, in connected <a href="/wiki/Handwriting_recognition" title="Handwriting recognition">handwriting recognition</a>.<a href="#cite_note-96">[96]</a><a href="#cite_note-SCHIDHUB-9">[9]</a> In 2006, publications by <a href="/wiki/Geoffrey_Hinton" title="Geoffrey Hinton">Geoff Hinton</a>, <a href="/wiki/Russ_Salakhutdinov" class="mw-redirect" title="Russ Salakhutdinov">Ruslan Salakhutdinov</a>, Osindero and <a href="/wiki/Yee_Whye_Teh" title="Yee Whye Teh">Teh</a><a href="#cite_note-97">[97]</a><a href="#cite_note-hinton06-98">[98]</a> <a href="/wiki/Deep_belief_network" title="Deep belief network">deep belief networks</a> were developed for generative modeling. They are trained by training one restricted Boltzmann machine, then freezing it and training another one on top of the first one, and so on, then optionally <a href="/wiki/Fine-tuning_(deep_learning)" title="Fine-tuning (deep learning)">fine-tuned</a> using supervised backpropagation.<a href="#cite_note-HINTON2007-99">[99]</a> They could model high-dimensional probability distributions, such as the distribution of <a href="/wiki/MNIST_database" title="MNIST database">MNIST images</a>, but convergence was slow.<a href="#cite_note-100">[100]</a><a href="#cite_note-101">[101]</a><a href="#cite_note-102">[102]</a> The impact of deep learning in industry began in the early 2000s, when CNNs already processed an estimated 10% to 20% of all the checks written in the US, according to Yann LeCun.<a href="#cite_note-lecun2016slides-103">[103]</a> Industrial applications of deep learning to large-scale speech recognition started around 2010. The 2009 NIPS Workshop on Deep Learning for Speech Recognition was motivated by the limitations of deep generative models of speech, and the possibility that given more capable hardware and large-scale data sets that deep neural nets might become practical. It was believed that pre-training DNNs using generative models of deep belief nets (DBN) would overcome the main difficulties of neural nets. However, it was discovered that replacing pre-training with large amounts of training data for straightforward backpropagation when using DNNs with large, context-dependent output layers produced error rates dramatically lower than then-state-of-the-art Gaussian mixture model (GMM)/Hidden Markov Model (HMM) and also than more-advanced generative model-based systems.<a href="#cite_note-HintonDengYu2012-104">[104]</a> The nature of the recognition errors produced by the two types of systems was characteristically different,<a href="#cite_note-ReferenceICASSP2013-105">[105]</a> offering technical insights into how to integrate deep learning into the existing highly efficient, run-time speech decoding system deployed by all major speech recognition systems.<a href="#cite_note-BOOK2014-23">[23]</a><a href="#cite_note-ReferenceA-106">[106]</a><a href="#cite_note-107">[107]</a> Analysis around 2009–2010, contrasting the GMM (and other generative speech models) vs. DNN models, stimulated early industrial investment in deep learning for speech recognition.<a href="#cite_note-ReferenceICASSP2013-105">[105]</a> That analysis was done with comparable performance (less than 1.5% in error rate) between discriminative DNNs and generative models.<a href="#cite_note-HintonDengYu2012-104">[104]</a><a href="#cite_note-ReferenceICASSP2013-105">[105]</a><a href="#cite_note-interspeech2014Keynote-108">[108]</a> In 2010, researchers extended deep learning from <a href="/wiki/TIMIT" title="TIMIT">TIMIT</a> to large vocabulary speech recognition, by adopting large output layers of the DNN based on context-dependent HMM states constructed by <a href="/wiki/Decision_tree" title="Decision tree">decision trees</a>.<a href="#cite_note-Roles2010-109">[109]</a><a href="#cite_note-110">[110]</a><a href="#cite_note-111">[111]</a><a href="#cite_note-ReferenceA-106">[106]</a> <div class="mw-heading mw-heading3"><h3 id="Deep_learning_revolution">Deep learning revolution</h3>[<a href="/w/index.php?title=Deep_learning&action=edit&section=7" title="Edit section: Deep learning revolution">edit</a>]</div> <figure class="mw-default-size" typeof="mw:File/Thumb"><a href="/wiki/File:AI-ML-DL.svg" class="mw-file-description"><img src="//upload.wikimedia.org/wikipedia/commons/thumb/b/bb/AI-ML-DL.svg/220px-AI-ML-DL.svg.png" decoding="async" width="220" height="243" class="mw-file-element" srcset="//upload.wikimedia.org/wikipedia/commons/thumb/b/bb/AI-ML-DL.svg/330px-AI-ML-DL.svg.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/b/bb/AI-ML-DL.svg/440px-AI-ML-DL.svg.png 2x" data-file-width="748" data-file-height="826" /></a><figcaption>How deep learning is a subset of machine learning and how machine learning is a subset of artificial intelligence (AI)</figcaption></figure> The deep learning revolution started around CNN- and GPU-based computer vision. Although CNNs trained by backpropagation had been around for decades and GPU implementations of NNs for years,<a href="#cite_note-jung2004-112">[112]</a> including CNNs,<a href="#cite_note-chellapilla2006-113">[113]</a> faster implementations of CNNs on GPUs were needed to progress on computer vision. Later, as deep learning becomes widespread, specialized hardware and algorithm optimizations were developed specifically for deep learning.<a href="#cite_note-sze2017-114">[114]</a> A key advance for the deep learning revolution was hardware advances, especially GPU. Some early work dated back to 2004.<a href="#cite_note-jung2004-112">[112]</a><a href="#cite_note-chellapilla2006-113">[113]</a> In 2009, Raina, Madhavan, and <a href="/wiki/Andrew_Ng" title="Andrew Ng">Andrew Ng</a> reported a 100M deep belief network trained on 30 Nvidia <a href="/wiki/GeForce_GTX_280" class="mw-redirect" title="GeForce GTX 280">GeForce GTX 280</a> GPUs, an early demonstration of GPU-based deep learning. They reported up to 70 times faster training.<a href="#cite_note-115">[115]</a> In 2011, a CNN named DanNet<a href="#cite_note-:3-116">[116]</a><a href="#cite_note-:6-117">[117]</a> by Dan Ciresan, Ueli Meier, Jonathan Masci, <a href="/wiki/Luca_Maria_Gambardella" title="Luca Maria Gambardella">Luca Maria Gambardella</a>, and <a href="/wiki/J%C3%BCrgen_Schmidhuber" title="Jürgen Schmidhuber">Jürgen Schmidhuber</a> achieved for the first time superhuman performance in a visual pattern recognition contest, outperforming traditional methods by a factor of 3.<a href="#cite_note-SCHIDHUB-9">[9]</a> It then won more contests.<a href="#cite_note-:8-118">[118]</a><a href="#cite_note-ciresan2013miccai-119">[119]</a> They also showed how <a href="/wiki/Max_pooling" class="mw-redirect" title="Max pooling">max-pooling</a> CNNs on GPU improved performance significantly.<a href="#cite_note-:9-3">[3]</a> In 2012, <a href="/wiki/Andrew_Ng" title="Andrew Ng">Andrew Ng</a> and <a href="/wiki/Jeff_Dean_(computer_scientist)" class="mw-redirect" title="Jeff Dean (computer scientist)">Jeff Dean</a> created an FNN that learned to recognize higher-level concepts, such as cats, only from watching unlabeled images taken from <a href="/wiki/YouTube" title="YouTube">YouTube</a> videos.<a href="#cite_note-ng2012-120">[120]</a> In October 2012, <a href="/wiki/AlexNet" title="AlexNet">AlexNet</a> by <a href="/wiki/Alex_Krizhevsky" title="Alex Krizhevsky">Alex Krizhevsky</a>, <a href="/wiki/Ilya_Sutskever" title="Ilya Sutskever">Ilya Sutskever</a>, and <a href="/wiki/Geoffrey_Hinton" title="Geoffrey Hinton">Geoffrey Hinton</a><a href="#cite_note-krizhevsky2012-4">[4]</a> won the large-scale <a href="/wiki/ImageNet_competition" class="mw-redirect" title="ImageNet competition">ImageNet competition</a> by a significant margin over shallow machine learning methods. Further incremental improvements included the <a href="/wiki/VGG-19" class="mw-redirect" title="VGG-19">VGG-16</a> network by <a href="/w/index.php?title=Karen_Simonyan_(scientist)&action=edit&redlink=1" class="new" title="Karen Simonyan (scientist) (page does not exist)">Karen Simonyan</a> and <a href="/wiki/Andrew_Zisserman" title="Andrew Zisserman">Andrew Zisserman</a><a href="#cite_note-VGG-121">[121]</a> and Google's <a href="/wiki/Inceptionv3" class="mw-redirect" title="Inceptionv3">Inceptionv3</a>.<a href="#cite_note-szegedy-122">[122]</a> The success in image classification was then extended to the more challenging task of <a href="/wiki/Automatic_image_annotation" title="Automatic image annotation">generating descriptions</a> (captions) for images, often as a combination of CNNs and LSTMs.<a href="#cite_note-1411.4555-123">[123]</a><a href="#cite_note-1411.4952-124">[124]</a><a href="#cite_note-1411.2539-125">[125]</a> In 2014, the state of the art was training “very deep neural network” with 20 to 30 layers.<a href="#cite_note-126">[126]</a> Stacking too many layers led to a steep reduction in <a href="/wiki/Training,_validation,_and_test_data_sets" title="Training, validation, and test data sets">training</a> accuracy,<a href="#cite_note-prelu-127">[127]</a> known as the "degradation" problem.<a href="#cite_note-resnet-128">[128]</a> In 2015, two techniques were developed to train very deep networks: the Highway Network was published in May 2015, and the <a href="/wiki/Residual_neural_network" title="Residual neural network">residual neural network</a> (ResNet)<a href="#cite_note-resnet20152-129">[129]</a> in Dec 2015. ResNet behaves like an open-gated Highway Net. Around the same time, deep learning started impacting the field of art. Early examples included <a href="/wiki/DeepDream" title="DeepDream">Google DeepDream</a> (2015), and <a href="/wiki/Neural_style_transfer" title="Neural style transfer">neural style transfer</a> (2015),<a href="#cite_note-130">[130]</a> both of which were based on pretrained image classification neural networks, such as <a href="/wiki/VGG-19" class="mw-redirect" title="VGG-19">VGG-19</a>. <a href="/wiki/Generative_adversarial_network" title="Generative adversarial network">Generative adversarial network</a> (GAN) by (<a href="/wiki/Ian_Goodfellow" title="Ian Goodfellow">Ian Goodfellow</a> et al., 2014)<a href="#cite_note-GANnips-131">[131]</a> (based on <a href="/wiki/J%C3%BCrgen_Schmidhuber" title="Jürgen Schmidhuber">Jürgen Schmidhuber</a>'s principle of artificial curiosity<a href="#cite_note-curiosity1991-74">[74]</a><a href="#cite_note-gancurpm2020-76">[76]</a>) became state of the art in generative modeling during 2014-2018 period. Excellent image quality is achieved by <a href="/wiki/Nvidia" title="Nvidia">Nvidia</a>'s <a href="/wiki/StyleGAN" title="StyleGAN">StyleGAN</a> (2018)<a href="#cite_note-SyncedReview2018-132">[132]</a> based on the Progressive GAN by Tero Karras et al.<a href="#cite_note-progressiveGAN2017-133">[133]</a> Here the GAN generator is grown from small to large scale in a pyramidal fashion. Image generation by GAN reached popular success, and provoked discussions concerning <a href="/wiki/Deepfake" title="Deepfake">deepfakes</a>.<a href="#cite_note-134">[134]</a> <a href="/wiki/Diffusion_model" title="Diffusion model">Diffusion models</a> (2015)<a href="#cite_note-135">[135]</a> eclipsed GANs in generative modeling since then, with systems such as <a href="/wiki/DALL-E" title="DALL-E">DALL·E 2</a> (2022) and <a href="/wiki/Stable_Diffusion" title="Stable Diffusion">Stable Diffusion</a> (2022). In 2015, Google's speech recognition improved by 49% by an LSTM-based model, which they made available through <a href="/wiki/Google_Voice_Search" title="Google Voice Search">Google Voice Search</a> on <a href="/wiki/Smartphone" title="Smartphone">smartphone</a>.<a href="#cite_note-GoogleVoiceTranscription-136">[136]</a><a href="#cite_note-sak2015-137">[137]</a> Deep learning is part of state-of-the-art systems in various disciplines, particularly computer vision and <a href="/wiki/Automatic_speech_recognition" class="mw-redirect" title="Automatic speech recognition">automatic speech recognition</a> (ASR). Results on commonly used evaluation sets such as <a href="/wiki/TIMIT" title="TIMIT">TIMIT</a> (ASR) and <a href="/wiki/MNIST_database" title="MNIST database">MNIST</a> (<a href="/wiki/Image_classification" class="mw-redirect" title="Image classification">image classification</a>), as well as a range of large-vocabulary speech recognition tasks have steadily improved.<a href="#cite_note-HintonDengYu2012-104">[104]</a><a href="#cite_note-138">[138]</a> Convolutional neural networks were superseded for ASR by <a href="/wiki/LSTM" class="mw-redirect" title="LSTM">LSTM</a>.<a href="#cite_note-sak2015-137">[137]</a><a href="#cite_note-sak2014-139">[139]</a><a href="#cite_note-liwu2015-140">[140]</a><a href="#cite_note-zen2015-141">[141]</a> but are more successful in computer vision. <a href="/wiki/Yoshua_Bengio" title="Yoshua Bengio">Yoshua Bengio</a>, <a href="/wiki/Geoffrey_Hinton" title="Geoffrey Hinton">Geoffrey Hinton</a> and <a href="/wiki/Yann_LeCun" title="Yann LeCun">Yann LeCun</a> were awarded the 2018 <a href="/wiki/Turing_Award" title="Turing Award">Turing Award</a> for "conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing".<a href="#cite_note-142">[142]</a> <div class="mw-heading mw-heading2"><h2 id="Neural_networks">Neural networks</h2>[<a href="/w/index.php?title=Deep_learning&action=edit&section=8" title="Edit section: Neural networks">edit</a>]</div> <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1236090951" /><div role="note" class="hatnote navigation-not-searchable">Main article: <a href="/wiki/Artificial_neural_network" class="mw-redirect" title="Artificial neural network">Artificial neural network</a></div> <style data-mw-deduplicate="TemplateStyles:r1273380762/mw-parser-output/.tmulti">.mw-parser-output .tmulti .multiimageinner{display:flex;flex-direction:column}.mw-parser-output .tmulti .trow{display:flex;flex-direction:row;clear:left;flex-wrap:wrap;width:100%;box-sizing:border-box}.mw-parser-output .tmulti .tsingle{margin:1px;float:left}.mw-parser-output .tmulti .theader{clear:both;font-weight:bold;text-align:center;align-self:center;background-color:transparent;width:100%}.mw-parser-output .tmulti .thumbcaption{background-color:transparent}.mw-parser-output .tmulti .text-align-left{text-align:left}.mw-parser-output .tmulti .text-align-right{text-align:right}.mw-parser-output .tmulti .text-align-center{text-align:center}@media all and (max-width:720px){.mw-parser-output .tmulti .thumbinner{width:100%!important;box-sizing:border-box;max-width:none!important;align-items:center}.mw-parser-output .tmulti .trow{justify-content:center}.mw-parser-output .tmulti .tsingle{float:none!important;max-width:100%!important;box-sizing:border-box;text-align:center}.mw-parser-output .tmulti .tsingle .thumbcaption{text-align:left}.mw-parser-output .tmulti .trow>.thumbcaption{text-align:center}}@media screen{html.skin-theme-clientpref-night .mw-parser-output .tmulti .multiimageinner span:not(.skin-invert-image):not(.skin-invert):not(.bg-transparent) img{background-color:white}}@media screen and (prefers-color-scheme:dark){html.skin-theme-clientpref-os .mw-parser-output .tmulti .multiimageinner span:not(.skin-invert-image):not(.skin-invert):not(.bg-transparent) img{background-color:white}}</style><div class="thumb tmulti tright"><div class="thumbinner multiimageinner" style="width:392px;max-width:392px"><div class="trow"><div class="tsingle" style="width:167px;max-width:167px"><div class="thumbimage" style="height:188px;overflow:hidden"><a href="/wiki/File:Simplified_neural_network_training_example.svg" class="mw-file-description"><img alt="" src="//upload.wikimedia.org/wikipedia/commons/thumb/0/0c/Simplified_neural_network_training_example.svg/165px-Simplified_neural_network_training_example.svg.png" decoding="async" width="165" height="189" class="mw-file-element" srcset="//upload.wikimedia.org/wikipedia/commons/thumb/0/0c/Simplified_neural_network_training_example.svg/248px-Simplified_neural_network_training_example.svg.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/0/0c/Simplified_neural_network_training_example.svg/330px-Simplified_neural_network_training_example.svg.png 2x" data-file-width="773" data-file-height="884" /></a></div><div class="thumbcaption">Simplified example of training a neural network in object detection: The network is trained by multiple images that are known to depict <a href="/wiki/Starfish" title="Starfish">starfish</a> and <a href="/wiki/Sea_urchin" title="Sea urchin">sea urchins</a>, which are correlated with "nodes" that represent visual <a href="/wiki/Feature_(computer_vision)" title="Feature (computer vision)">features</a>. The starfish match with a ringed texture and a star outline, whereas most sea urchins match with a striped texture and oval shape. However, the instance of a ring textured sea urchin creates a weakly weighted association between them.</div></div><div class="tsingle" style="width:221px;max-width:221px"><div class="thumbimage" style="height:188px;overflow:hidden"><a href="/wiki/File:Simplified_neural_network_example.svg" class="mw-file-description"><img alt="" src="//upload.wikimedia.org/wikipedia/commons/thumb/b/b7/Simplified_neural_network_example.svg/219px-Simplified_neural_network_example.svg.png" decoding="async" width="219" height="188" class="mw-file-element" srcset="//upload.wikimedia.org/wikipedia/commons/thumb/b/b7/Simplified_neural_network_example.svg/329px-Simplified_neural_network_example.svg.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/b/b7/Simplified_neural_network_example.svg/438px-Simplified_neural_network_example.svg.png 2x" data-file-width="1028" data-file-height="882" /></a></div><div class="thumbcaption">Subsequent run of the network on an input image (left):<a href="#cite_note-143">[143]</a> The network correctly detects the starfish. However, the weakly weighted association between ringed texture and sea urchin also confers a weak signal to the latter from one of two intermediate nodes. In addition, a <a href="/wiki/Seashell" title="Seashell">shell</a> that was not included in the training gives a weak signal for the oval shape, also resulting in a weak signal for the sea urchin output. These weak signals may result in a <a href="/wiki/False_positive" class="mw-redirect" title="False positive">false positive</a> result for sea urchin. In reality, textures and outlines would not be represented by single nodes, but rather by associated weight patterns of multiple nodes.</div></div></div></div></div> Artificial neural networks (ANNs) or <a href="/wiki/Connectionism" title="Connectionism">connectionist</a> systems are computing systems inspired by the <a href="/wiki/Biological_neural_network" class="mw-redirect" title="Biological neural network">biological neural networks</a> that constitute animal brains. Such systems learn (progressively improve their ability) to do tasks by considering examples, generally without task-specific programming. For example, in image recognition, they might learn to identify images that contain cats by analyzing example images that have been manually <a href="/wiki/Labeled_data" title="Labeled data">labeled</a> as "cat" or "no cat" and using the analytic results to identify cats in other images. They have found most use in applications difficult to express with a traditional computer algorithm using <a href="/wiki/Rule-based_programming" class="mw-redirect" title="Rule-based programming">rule-based programming</a>. An ANN is based on a collection of connected units called <a href="/wiki/Artificial_neuron" title="Artificial neuron">artificial neurons</a>, (analogous to biological <a href="/wiki/Neuron" title="Neuron">neurons</a> in a <a href="/wiki/Brain" title="Brain">biological brain</a>). Each connection (<a href="/wiki/Synapse" title="Synapse">synapse</a>) between neurons can transmit a signal to another neuron. The receiving (postsynaptic) neuron can process the signal(s) and then signal downstream neurons connected to it. Neurons may have state, generally represented by <a href="/wiki/Real_numbers" class="mw-redirect" title="Real numbers">real numbers</a>, typically between 0 and 1. Neurons and synapses may also have a weight that varies as learning proceeds, which can increase or decrease the strength of the signal that it sends downstream. Typically, neurons are organized in layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first (input), to the last (output) layer, possibly after traversing the layers multiple times. The original goal of the neural network approach was to solve problems in the same way that a human brain would. Over time, attention focused on matching specific mental abilities, leading to deviations from biology such as <a href="/wiki/Backpropagation" title="Backpropagation">backpropagation</a>, or passing information in the reverse direction and adjusting the network to reflect that information. Neural networks have been used on a variety of tasks, including computer vision, <a href="/wiki/Speech_recognition" title="Speech recognition">speech recognition</a>, <a href="/wiki/Machine_translation" title="Machine translation">machine translation</a>, <a href="/wiki/Social_network" title="Social network">social network</a> filtering, <a href="/wiki/General_game_playing" title="General game playing">playing board and video games</a> and medical diagnosis. As of 2017, neural networks typically have a few thousand to a few million units and millions of connections. Despite this number being several order of magnitude less than the number of neurons on a human brain, these networks can perform many tasks at a level beyond that of humans (e.g., recognizing faces, or playing "Go"<a href="#cite_note-144">[144]</a>). <div class="mw-heading mw-heading3"><h3 id="Deep_neural_networks">Deep neural networks</h3>[<a href="/w/index.php?title=Deep_learning&action=edit&section=9" title="Edit section: Deep neural networks">edit</a>]</div> A deep neural network (DNN) is an artificial neural network with multiple layers between the input and output layers.<a href="#cite_note-BENGIODEEP-7">[7]</a><a href="#cite_note-SCHIDHUB-9">[9]</a> There are different types of neural networks but they always consist of the same components: neurons, synapses, weights, biases, and functions.<a href="#cite_note-Nokkada-145">[145]</a> These components as a whole function in a way that mimics functions of the human brain, and can be trained like any other ML algorithm.[<a href="/wiki/Wikipedia:Citation_needed" title="Wikipedia:Citation needed">citation needed</a>] For example, a DNN that is trained to recognize dog breeds will go over the given image and calculate the probability that the dog in the image is a certain breed. The user can review the results and select which probabilities the network should display (above a certain threshold, etc.) and return the proposed label. Each mathematical manipulation as such is considered a layer, <a href="#cite_note-Kumar2021-146">[146]</a> and complex DNN have many layers, hence the name "deep" networks. DNNs can model complex non-linear relationships. DNN architectures generate compositional models where the object is expressed as a layered composition of <a href="/wiki/Primitive_data_type" title="Primitive data type">primitives</a>.<a href="#cite_note-147">[147]</a> The extra layers enable composition of features from lower layers, potentially modeling complex data with fewer units than a similarly performing shallow network.<a href="#cite_note-BENGIODEEP-7">[7]</a> For instance, it was proved that sparse <a href="/wiki/Multivariate_polynomial" class="mw-redirect" title="Multivariate polynomial">multivariate polynomials</a> are exponentially easier to approximate with DNNs than with shallow networks.<a href="#cite_note-rolnickpaper-148">[148]</a> Deep architectures include many variants of a few basic approaches. Each architecture has found success in specific domains. It is not always possible to compare the performance of multiple architectures, unless they have been evaluated on the same data sets.<a href="#cite_note-Kumar2021-146">[146]</a> DNNs are typically feedforward networks in which data flows from the input layer to the output layer without looping back. At first, the DNN creates a map of virtual neurons and assigns random numerical values, or "weights", to connections between them. The weights and inputs are multiplied and return an output between 0 and 1. If the network did not accurately recognize a particular pattern, an algorithm would adjust the weights.<a href="#cite_note-149">[149]</a> That way the algorithm can make certain parameters more influential, until it determines the correct mathematical manipulation to fully process the data. <a href="/wiki/Recurrent_neural_networks" class="mw-redirect" title="Recurrent neural networks">Recurrent neural networks</a>, in which data can flow in any direction, are used for applications such as <a href="/wiki/Language_model" title="Language model">language modeling</a>.<a href="#cite_note-gers2001-150">[150]</a><a href="#cite_note-NIPS2014-151">[151]</a><a href="#cite_note-vinyals2016-152">[152]</a><a href="#cite_note-gillick2015-153">[153]</a><a href="#cite_note-MIKO2010-154">[154]</a> Long short-term memory is particularly effective for this use.<a href="#cite_note-:0-155">[155]</a><a href="#cite_note-:10-156">[156]</a> <a href="/wiki/Convolutional_neural_network" title="Convolutional neural network">Convolutional neural networks</a> (CNNs) are used in computer vision.<a href="#cite_note-LECUN86-157">[157]</a> CNNs also have been applied to <a href="/wiki/Acoustic_model" title="Acoustic model">acoustic modeling</a> for automatic speech recognition (ASR).<a href="#cite_note-:2-158">[158]</a> <div class="mw-heading mw-heading4"><h4 id="Challenges">Challenges</h4>[<a href="/w/index.php?title=Deep_learning&action=edit&section=10" title="Edit section: Challenges">edit</a>]</div> As with ANNs, many issues can arise with naively trained DNNs. Two common issues are <a href="/wiki/Overfitting" title="Overfitting">overfitting</a> and computation time. DNNs are prone to overfitting because of the added layers of abstraction, which allow them to model rare dependencies in the training data. <a href="/wiki/Regularization_(mathematics)" title="Regularization (mathematics)">Regularization</a> methods such as Ivakhnenko's unit pruning<a href="#cite_note-ivak1971-41">[41]</a> or <a href="/wiki/Weight_decay" class="mw-redirect" title="Weight decay">weight decay</a> (<math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle \ell _{2}}"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <msub> <mi>ℓ</mi> <mrow class="MJX-TeXAtom-ORD"> <mn>2</mn> </mrow> </msub> </mstyle> </mrow> <annotation encoding="application/x-tex">{\displaystyle \ell _{2}}</annotation> </semantics> </math><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/85a4571ee9be10bd3c9df2480ab3d280f99e801a" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.671ex; width:2.024ex; height:2.509ex;" alt="{\displaystyle \ell _{2}}" />-regularization) or <a href="/wiki/Sparse_matrix" title="Sparse matrix">sparsity</a> (<math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle \ell _{1}}"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <msub> <mi>ℓ</mi> <mrow class="MJX-TeXAtom-ORD"> <mn>1</mn> </mrow> </msub> </mstyle> </mrow> <annotation encoding="application/x-tex">{\displaystyle \ell _{1}}</annotation> </semantics> </math><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/361ddd720474aa41cb05453e03424fb7999d3b02" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.671ex; width:2.024ex; height:2.509ex;" alt="{\displaystyle \ell _{1}}" />-regularization) can be applied during training to combat overfitting.<a href="#cite_note-159">[159]</a> Alternatively <a href="/wiki/Dropout_(neural_networks)" class="mw-redirect" title="Dropout (neural networks)">dropout</a> regularization randomly omits units from the hidden layers during training. This helps to exclude rare dependencies.<a href="#cite_note-DAHL2013-160">[160]</a> Another interesting recent development is research into models of just enough complexity through an estimation of the intrinsic complexity of the task being modelled. This approach has been successfully applied for multivariate time series prediction tasks such as traffic prediction.<a href="#cite_note-Kumar2024-161">[161]</a> Finally, data can be augmented via methods such as cropping and rotating such that smaller training sets can be increased in size to reduce the chances of overfitting.<a href="#cite_note-162">[162]</a> DNNs must consider many training parameters, such as the size (number of layers and number of units per layer), the <a href="/wiki/Learning_rate" title="Learning rate">learning rate</a>, and initial weights. <a href="/wiki/Hyperparameter_optimization#Grid_search" title="Hyperparameter optimization">Sweeping through the parameter space</a> for optimal parameters may not be feasible due to the cost in time and computational resources. Various tricks, such as <a href="/wiki/Batch_learning" class="mw-redirect" title="Batch learning">batching</a> (computing the gradient on several training examples at once rather than individual examples)<a href="#cite_note-RBMTRAIN-163">[163]</a> speed up computation. Large processing capabilities of many-core architectures (such as GPUs or the Intel Xeon Phi) have produced significant speedups in training, because of the suitability of such processing architectures for the matrix and vector computations.<a href="#cite_note-164">[164]</a><a href="#cite_note-165">[165]</a> Alternatively, engineers may look for other types of neural networks with more straightforward and convergent training algorithms. CMAC (<a href="/wiki/Cerebellar_model_articulation_controller" title="Cerebellar model articulation controller">cerebellar model articulation controller</a>) is one such kind of neural network. It doesn't require learning rates or randomized initial weights. The training process can be guaranteed to converge in one step with a new batch of data, and the computational complexity of the training algorithm is linear with respect to the number of neurons involved.<a href="#cite_note-Qin1-166">[166]</a><a href="#cite_note-Qin2-167">[167]</a> <div class="mw-heading mw-heading2"><h2 id="Hardware">Hardware</h2>[<a href="/w/index.php?title=Deep_learning&action=edit&section=11" title="Edit section: Hardware">edit</a>]</div> Since the 2010s, advances in both machine learning algorithms and <a href="/wiki/Computer_hardware" title="Computer hardware">computer hardware</a> have led to more efficient methods for training deep neural networks that contain many layers of non-linear hidden units and a very large output layer.<a href="#cite_note-168">[168]</a> By 2019, graphics processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method for training large-scale commercial cloud AI .<a href="#cite_note-169">[169]</a> <a href="/wiki/OpenAI" title="OpenAI">OpenAI</a> estimated the hardware computation used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017) and found a 300,000-fold increase in the amount of computation required, with a doubling-time trendline of 3.4 months.<a href="#cite_note-170">[170]</a><a href="#cite_note-171">[171]</a> Special <a href="/wiki/Electronic_circuit" title="Electronic circuit">electronic circuits</a> called <a href="/wiki/Deep_learning_processor" class="mw-redirect" title="Deep learning processor">deep learning processors</a> were designed to speed up deep learning algorithms. Deep learning processors include neural processing units (NPUs) in <a href="/wiki/Huawei" title="Huawei">Huawei</a> cellphones<a href="#cite_note-172">[172]</a> and <a href="/wiki/Cloud_computing" title="Cloud computing">cloud computing</a> servers such as <a href="/wiki/Tensor_processing_unit" class="mw-redirect" title="Tensor processing unit">tensor processing units</a> (TPU) in the <a href="/wiki/Google_Cloud_Platform" title="Google Cloud Platform">Google Cloud Platform</a>.<a href="#cite_note-173">[173]</a> <a href="/wiki/Cerebras" title="Cerebras">Cerebras Systems</a> has also built a dedicated system to handle large deep learning models, the CS-2, based on the largest processor in the industry, the second-generation Wafer Scale Engine (WSE-2).<a href="#cite_note-174">[174]</a><a href="#cite_note-175">[175]</a> Atomically thin <a href="/wiki/Semiconductors" class="mw-redirect" title="Semiconductors">semiconductors</a> are considered promising for energy-efficient deep learning hardware where the same basic device structure is used for both logic operations and data storage. In 2020, Marega et al. published experiments with a large-area active channel material for developing logic-in-memory devices and circuits based on <a href="/wiki/Floating-gate" class="mw-redirect" title="Floating-gate">floating-gate</a> <a href="/wiki/Field-effect_transistor" title="Field-effect transistor">field-effect transistors</a> (FGFETs).<a href="#cite_note-atomthin-176">[176]</a> In 2021, J. Feldmann et al. proposed an integrated <a href="/wiki/Photonic" class="mw-redirect" title="Photonic">photonic</a> <a href="/wiki/Hardware_accelerator" class="mw-redirect" title="Hardware accelerator">hardware accelerator</a> for parallel convolutional processing.<a href="#cite_note-photonic-177">[177]</a> The authors identify two key advantages of integrated photonics over its electronic counterparts: (1) massively parallel data transfer through <a href="/wiki/Wavelength" title="Wavelength">wavelength</a> division <a href="/wiki/Multiplexing" title="Multiplexing">multiplexing</a> in conjunction with <a href="/wiki/Frequency_comb" title="Frequency comb">frequency combs</a>, and (2) extremely high data modulation speeds.<a href="#cite_note-photonic-177">[177]</a> Their system can execute trillions of multiply-accumulate operations per second, indicating the potential of <a href="/wiki/Photonic_integrated_circuit" title="Photonic integrated circuit">integrated</a> <a href="/wiki/Photonics" title="Photonics">photonics</a> in data-heavy AI applications.<a href="#cite_note-photonic-177">[177]</a> <div class="mw-heading mw-heading2"><h2 id="Applications">Applications</h2>[<a href="/w/index.php?title=Deep_learning&action=edit&section=12" title="Edit section: Applications">edit</a>]</div> <div class="mw-heading mw-heading3"><h3 id="Automatic_speech_recognition">Automatic speech recognition</h3>[<a href="/w/index.php?title=Deep_learning&action=edit&section=13" title="Edit section: Automatic speech recognition">edit</a>]</div> <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1236090951" /><div role="note" class="hatnote navigation-not-searchable">Main article: <a href="/wiki/Speech_recognition" title="Speech recognition">Speech recognition</a></div> Large-scale automatic speech recognition is the first and most convincing successful case of deep learning. LSTM RNNs can learn "Very Deep Learning" tasks<a href="#cite_note-SCHIDHUB-9">[9]</a> that involve multi-second intervals containing speech events separated by thousands of discrete time steps, where one time step corresponds to about 10 ms. LSTM with forget gates<a href="#cite_note-:10-156">[156]</a> is competitive with traditional speech recognizers on certain tasks.<a href="#cite_note-graves2003-93">[93]</a> The initial success in speech recognition was based on small-scale recognition tasks based on TIMIT. The data set contains 630 speakers from eight major <a href="/wiki/Dialect" title="Dialect">dialects</a> of <a href="/wiki/American_English" title="American English">American English</a>, where each speaker reads 10 sentences.<a href="#cite_note-APC_1-178">[178]</a> Its small size lets many configurations be tried. More importantly, the TIMIT task concerns <a href="/wiki/Phone_(phonetics)" title="Phone (phonetics)">phone</a>-sequence recognition, which, unlike word-sequence recognition, allows weak phone <a href="/wiki/Bigram" title="Bigram">bigram</a> language models. This lets the strength of the acoustic modeling aspects of speech recognition be more easily analyzed. The error rates listed below, including these early results and measured as percent phone error rates (PER), have been summarized since 1991. <table class="wikitable"> <tbody><tr> <th>Method</th> <th>Percent phone error rate (PER) (%) </th></tr> <tr> <td>Randomly Initialized RNN<a href="#cite_note-179">[179]</a></td> <td>26.1 </td></tr> <tr> <td>Bayesian Triphone GMM-HMM</td> <td>25.6 </td></tr> <tr> <td>Hidden Trajectory (Generative) Model</td> <td>24.8 </td></tr> <tr> <td>Monophone Randomly Initialized DNN</td> <td>23.4 </td></tr> <tr> <td>Monophone DBN-DNN</td> <td>22.4 </td></tr> <tr> <td>Triphone GMM-HMM with BMMI Training</td> <td>21.7 </td></tr> <tr> <td>Monophone DBN-DNN on fbank</td> <td>20.7 </td></tr> <tr> <td>Convolutional DNN<a href="#cite_note-CNN-2014-180">[180]</a></td> <td>20.0 </td></tr> <tr> <td>Convolutional DNN w. Heterogeneous Pooling</td> <td>18.7 </td></tr> <tr> <td>Ensemble DNN/CNN/RNN<a href="#cite_note-EnsembleDL-181">[181]</a></td> <td>18.3 </td></tr> <tr> <td>Bidirectional LSTM</td> <td>17.8 </td></tr> <tr> <td>Hierarchical Convolutional Deep Maxout Network<a href="#cite_note-HCDMM-182">[182]</a></td> <td>16.5 </td></tr></tbody></table> The debut of DNNs for speaker recognition in the late 1990s and speech recognition around 2009-2011 and of LSTM around 2003–2007, accelerated progress in eight major areas:<a href="#cite_note-BOOK2014-23">[23]</a><a href="#cite_note-interspeech2014Keynote-108">[108]</a><a href="#cite_note-ReferenceA-106">[106]</a> <ul><li>Scale-up/out and accelerated DNN training and decoding</li> <li>Sequence discriminative training</li> <li>Feature processing by deep models with solid understanding of the underlying mechanisms</li> <li>Adaptation of DNNs and related deep models</li> <li><a href="/wiki/Multi-task_learning" title="Multi-task learning">Multi-task</a> and <a href="/wiki/Transfer_learning" title="Transfer learning">transfer learning</a> by DNNs and related deep models</li> <li><a href="/wiki/Convolutional_neural_network" title="Convolutional neural network">CNNs</a> and how to design them to best exploit <a href="/wiki/Domain_knowledge" title="Domain knowledge">domain knowledge</a> of speech</li> <li><a href="/wiki/Recurrent_neural_network" title="Recurrent neural network">RNN</a> and its rich LSTM variants</li> <li>Other types of deep models including tensor-based models and integrated deep generative/discriminative models.</li></ul> All major commercial speech recognition systems (e.g., Microsoft <a href="/wiki/Cortana_(software)" class="mw-redirect" title="Cortana (software)">Cortana</a>, <a href="/wiki/Xbox" title="Xbox">Xbox</a>, <a href="/wiki/Skype_Translator" title="Skype Translator">Skype Translator</a>, <a href="/wiki/Amazon_Alexa" title="Amazon Alexa">Amazon Alexa</a>, <a href="/wiki/Google_Now" title="Google Now">Google Now</a>, <a href="/wiki/Siri" title="Siri">Apple Siri</a>, <a href="/wiki/Baidu" title="Baidu">Baidu</a> and <a href="/wiki/IFlytek" title="IFlytek">iFlyTek</a> voice search, and a range of <a href="/wiki/Nuance_Communications" title="Nuance Communications">Nuance</a> speech products, etc.) are based on deep learning.<a href="#cite_note-BOOK2014-23">[23]</a><a href="#cite_note-183">[183]</a><a href="#cite_note-Baidu-184">[184]</a> <div class="mw-heading mw-heading3"><h3 id="Image_recognition">Image recognition</h3>[<a href="/w/index.php?title=Deep_learning&action=edit&section=14" title="Edit section: Image recognition">edit</a>]</div> <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1236090951" /><div role="note" class="hatnote navigation-not-searchable">Main article: <a href="/wiki/Computer_vision" title="Computer vision">Computer vision</a></div> <figure class="mw-default-size" typeof="mw:File/Thumb"><video id="mwe_player_0" poster="//upload.wikimedia.org/wikipedia/commons/thumb/e/e2/Deep_Learning_in_mussel_farming.webm/220px--Deep_Learning_in_mussel_farming.webm.jpg" controls="" preload="none" data-mw-tmh="" class="mw-file-element" width="220" height="124" data-durationhint="41" data-mwtitle="Deep_Learning_in_mussel_farming.webm" data-mwprovider="wikimediacommons" resource="/wiki/File:Deep_Learning_in_mussel_farming.webm"><source src="//upload.wikimedia.org/wikipedia/commons/transcoded/e/e2/Deep_Learning_in_mussel_farming.webm/Deep_Learning_in_mussel_farming.webm.480p.vp9.webm" type="video/webm; codecs="vp9, opus"" data-transcodekey="480p.vp9.webm" data-width="854" data-height="480" /><source src="//upload.wikimedia.org/wikipedia/commons/transcoded/e/e2/Deep_Learning_in_mussel_farming.webm/Deep_Learning_in_mussel_farming.webm.720p.vp9.webm" type="video/webm; codecs="vp9, opus"" data-transcodekey="720p.vp9.webm" data-width="1280" data-height="720" /><source src="//upload.wikimedia.org/wikipedia/commons/transcoded/e/e2/Deep_Learning_in_mussel_farming.webm/Deep_Learning_in_mussel_farming.webm.1080p.vp9.webm" type="video/webm; codecs="vp9, opus"" data-transcodekey="1080p.vp9.webm" data-width="1920" data-height="1080" /><source src="//upload.wikimedia.org/wikipedia/commons/e/e2/Deep_Learning_in_mussel_farming.webm" type="video/webm; codecs="vp8, vorbis"" data-width="1920" data-height="1080" /><source src="//upload.wikimedia.org/wikipedia/commons/transcoded/e/e2/Deep_Learning_in_mussel_farming.webm/Deep_Learning_in_mussel_farming.webm.144p.mjpeg.mov" type="video/quicktime" data-transcodekey="144p.mjpeg.mov" data-width="256" data-height="144" /><source src="//upload.wikimedia.org/wikipedia/commons/transcoded/e/e2/Deep_Learning_in_mussel_farming.webm/Deep_Learning_in_mussel_farming.webm.240p.vp9.webm" type="video/webm; codecs="vp9, opus"" data-transcodekey="240p.vp9.webm" data-width="426" data-height="240" /><source src="//upload.wikimedia.org/wikipedia/commons/transcoded/e/e2/Deep_Learning_in_mussel_farming.webm/Deep_Learning_in_mussel_farming.webm.360p.vp9.webm" type="video/webm; codecs="vp9, opus"" data-transcodekey="360p.vp9.webm" data-width="640" data-height="360" /><source src="//upload.wikimedia.org/wikipedia/commons/transcoded/e/e2/Deep_Learning_in_mussel_farming.webm/Deep_Learning_in_mussel_farming.webm.360p.webm" type="video/webm; codecs="vp8, vorbis"" data-transcodekey="360p.webm" data-width="640" data-height="360" /></video><figcaption>Richard Green explains how deep learning is used with a <a href="/wiki/Remotely_operated_underwater_vehicle" title="Remotely operated underwater vehicle">remotely operated vehicle</a> in <a href="/wiki/Mussel#Aquaculture" title="Mussel">mussel aquaculture</a>.</figcaption></figure> A common evaluation set for image classification is the <a href="/wiki/MNIST_database" title="MNIST database">MNIST database</a> data set. MNIST is composed of handwritten digits and includes 60,000 training examples and 10,000 test examples. As with TIMIT, its small size lets users test multiple configurations. A comprehensive list of results on this set is available.<a href="#cite_note-YANNMNIST-185">[185]</a> Deep learning-based image recognition has become "superhuman", producing more accurate results than human contestants. This first occurred in 2011 in recognition of traffic signs, and in 2014, with recognition of human faces.<a href="#cite_note-:7-186">[186]</a><a href="#cite_note-surpass1-187">[187]</a> Deep learning-trained vehicles now interpret 360° camera views.<a href="#cite_note-188">[188]</a> Another example is Facial Dysmorphology Novel Analysis (FDNA) used to analyze cases of human malformation connected to a large database of genetic syndromes. <div class="mw-heading mw-heading3"><h3 id="Visual_art_processing">Visual art processing</h3>[<a href="/w/index.php?title=Deep_learning&action=edit&section=15" title="Edit section: Visual art processing">edit</a>]</div> <figure typeof="mw:File/Thumb"><a href="/wiki/File:Jimmy_Wales_in_France,_with_the_style_of_Munch%27s_%22The_Scream%22_applied_using_neural_style_transfer.jpg" class="mw-file-description"><img src="//upload.wikimedia.org/wikipedia/commons/thumb/7/75/Jimmy_Wales_in_France%2C_with_the_style_of_Munch%27s_%22The_Scream%22_applied_using_neural_style_transfer.jpg/142px-Jimmy_Wales_in_France%2C_with_the_style_of_Munch%27s_%22The_Scream%22_applied_using_neural_style_transfer.jpg" decoding="async" width="142" height="164" class="mw-file-element" srcset="//upload.wikimedia.org/wikipedia/commons/thumb/7/75/Jimmy_Wales_in_France%2C_with_the_style_of_Munch%27s_%22The_Scream%22_applied_using_neural_style_transfer.jpg/212px-Jimmy_Wales_in_France%2C_with_the_style_of_Munch%27s_%22The_Scream%22_applied_using_neural_style_transfer.jpg 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/7/75/Jimmy_Wales_in_France%2C_with_the_style_of_Munch%27s_%22The_Scream%22_applied_using_neural_style_transfer.jpg/283px-Jimmy_Wales_in_France%2C_with_the_style_of_Munch%27s_%22The_Scream%22_applied_using_neural_style_transfer.jpg 2x" data-file-width="1168" data-file-height="1352" /></a><figcaption>Visual art processing of Jimmy Wales in France, with the style of Munch's "<a href="/wiki/The_Scream" title="The Scream">The Scream</a>" applied using neural style transfer</figcaption></figure> Closely related to the progress that has been made in image recognition is the increasing application of deep learning techniques to various visual art tasks. DNNs have proven themselves capable, for example, of <ul><li>identifying the style period of a given painting<a href="#cite_note-art1-189">[189]</a><a href="#cite_note-art2-190">[190]</a></li> <li><a href="/wiki/Neural_Style_Transfer" class="mw-redirect" title="Neural Style Transfer">Neural Style Transfer</a> – capturing the style of a given artwork and applying it in a visually pleasing manner to an arbitrary photograph or video<a href="#cite_note-art1-189">[189]</a><a href="#cite_note-art2-190">[190]</a></li> <li>generating striking imagery based on random visual input fields.<a href="#cite_note-art1-189">[189]</a><a href="#cite_note-art2-190">[190]</a></li></ul> <div class="mw-heading mw-heading3"><h3 id="Natural_language_processing">Natural language processing</h3>[<a href="/w/index.php?title=Deep_learning&action=edit&section=16" title="Edit section: Natural language processing">edit</a>]</div> <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1236090951" /><div role="note" class="hatnote navigation-not-searchable">Main article: <a href="/wiki/Natural_language_processing" title="Natural language processing">Natural language processing</a></div> Neural networks have been used for implementing language models since the early 2000s.<a href="#cite_note-gers2001-150">[150]</a> LSTM helped to improve machine translation and language modeling.<a href="#cite_note-NIPS2014-151">[151]</a><a href="#cite_note-vinyals2016-152">[152]</a><a href="#cite_note-gillick2015-153">[153]</a> Other key techniques in this field are negative sampling<a href="#cite_note-GoldbergLevy2014-191">[191]</a> and <a href="/wiki/Word_embedding" title="Word embedding">word embedding</a>. Word embedding, such as <a href="/wiki/Word2vec" title="Word2vec">word2vec</a>, can be thought of as a representational layer in a deep learning architecture that transforms an atomic word into a positional representation of the word relative to other words in the dataset; the position is represented as a point in a <a href="/wiki/Vector_space" title="Vector space">vector space</a>. Using word embedding as an RNN input layer allows the network to parse sentences and phrases using an effective compositional vector grammar. A compositional vector grammar can be thought of as <a href="/wiki/Probabilistic_context_free_grammar" class="mw-redirect" title="Probabilistic context free grammar">probabilistic context free grammar</a> (PCFG) implemented by an RNN.<a href="#cite_note-SocherManning2014-192">[192]</a> Recursive auto-encoders built atop word embeddings can assess sentence similarity and detect paraphrasing.<a href="#cite_note-SocherManning2014-192">[192]</a> Deep neural architectures provide the best results for constituency parsing,<a href="#cite_note-193">[193]</a> <a href="/wiki/Sentiment_analysis" title="Sentiment analysis">sentiment analysis</a>,<a href="#cite_note-RDM_1-194">[194]</a> information retrieval,<a href="#cite_note-195">[195]</a><a href="#cite_note-196">[196]</a> spoken language understanding,<a href="#cite_note-IEEE-TASL2015-197">[197]</a> machine translation,<a href="#cite_note-NIPS2014-151">[151]</a><a href="#cite_note-auto-198">[198]</a> contextual entity linking,<a href="#cite_note-auto-198">[198]</a> writing style recognition,<a href="#cite_note-BROC2017-199">[199]</a> <a href="/wiki/Named-entity_recognition" title="Named-entity recognition">named-entity recognition</a> (token classification),<a href="#cite_note-200">[200]</a> text classification, and others.<a href="#cite_note-201">[201]</a> Recent developments generalize <a href="/wiki/Word_embedding" title="Word embedding">word embedding</a> to <a href="/wiki/Sentence_embedding" title="Sentence embedding">sentence embedding</a>. <a href="/wiki/Google_Translate" title="Google Translate">Google Translate</a> (GT) uses a large end-to-end <a href="/wiki/Long_short-term_memory" title="Long short-term memory">long short-term memory</a> (LSTM) network.<a href="#cite_note-GT_Turovsky_2016-202">[202]</a><a href="#cite_note-googleblog_GNMT_2016-203">[203]</a><a href="#cite_note-GoogleTranslate-204">[204]</a><a href="#cite_note-WiredGoogleTranslate-205">[205]</a> <a href="/wiki/Google_Neural_Machine_Translation" title="Google Neural Machine Translation">Google Neural Machine Translation (GNMT)</a> uses an <a href="/wiki/Example-based_machine_translation" title="Example-based machine translation">example-based machine translation</a> method in which the system "learns from millions of examples".<a href="#cite_note-googleblog_GNMT_2016-203">[203]</a> It translates "whole sentences at a time, rather than pieces". Google Translate supports over one hundred languages.<a href="#cite_note-googleblog_GNMT_2016-203">[203]</a> The network encodes the "semantics of the sentence rather than simply memorizing phrase-to-phrase translations".<a href="#cite_note-googleblog_GNMT_2016-203">[203]</a><a href="#cite_note-Biotet-206">[206]</a> GT uses English as an intermediate between most language pairs.<a href="#cite_note-Biotet-206">[206]</a> <div class="mw-heading mw-heading3"><h3 id="Drug_discovery_and_toxicology">Drug discovery and toxicology</h3>[<a href="/w/index.php?title=Deep_learning&action=edit&section=17" title="Edit section: Drug discovery and toxicology">edit</a>]</div> <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1236090951" /><div role="note" class="hatnote navigation-not-searchable">For more information, see <a href="/wiki/Drug_discovery" title="Drug discovery">Drug discovery</a> and <a href="/wiki/Toxicology" title="Toxicology">Toxicology</a>.</div> A large percentage of candidate drugs fail to win regulatory approval. These failures are caused by insufficient efficacy (on-target effect), undesired interactions (off-target effects), or unanticipated <a href="/wiki/Toxicity" title="Toxicity">toxic effects</a>.<a href="#cite_note-ARROWSMITH2013-207">[207]</a><a href="#cite_note-VERBIEST2015-208">[208]</a> Research has explored use of deep learning to predict the <a href="/wiki/Biomolecular_target" class="mw-redirect" title="Biomolecular target">biomolecular targets</a>,<a href="#cite_note-MERCK2012-209">[209]</a><a href="#cite_note-:5-210">[210]</a> <a href="/wiki/Off-target" class="mw-redirect" title="Off-target">off-targets</a>, and <a href="/wiki/Toxicity" title="Toxicity">toxic effects</a> of environmental chemicals in nutrients, household products and drugs.<a href="#cite_note-TOX21-211">[211]</a><a href="#cite_note-TOX21Data-212">[212]</a><a href="#cite_note-:11-213">[213]</a> AtomNet is a deep learning system for structure-based <a href="/wiki/Drug_design" title="Drug design">rational drug design</a>.<a href="#cite_note-214">[214]</a> AtomNet was used to predict novel candidate biomolecules for disease targets such as the <a href="/wiki/Ebola_virus" class="mw-redirect" title="Ebola virus">Ebola virus</a><a href="#cite_note-Toronto-215">[215]</a> and <a href="/wiki/Multiple_sclerosis" title="Multiple sclerosis">multiple sclerosis</a>.<a href="#cite_note-216">[216]</a><a href="#cite_note-Toronto-215">[215]</a> In 2017 <a href="/wiki/Graph_neural_network" title="Graph neural network">graph neural networks</a> were used for the first time to predict various properties of molecules in a large toxicology data set.<a href="#cite_note-217">[217]</a> In 2019, generative neural networks were used to produce molecules that were validated experimentally all the way into mice.<a href="#cite_note-218">[218]</a><a href="#cite_note-219">[219]</a> <div class="mw-heading mw-heading3"><h3 id="Customer_relationship_management">Customer relationship management</h3>[<a href="/w/index.php?title=Deep_learning&action=edit&section=18" title="Edit section: Customer relationship management">edit</a>]</div> <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1236090951" /><div role="note" class="hatnote navigation-not-searchable">Main article: <a href="/wiki/Customer_relationship_management" title="Customer relationship management">Customer relationship management</a></div> <a href="/wiki/Deep_reinforcement_learning" title="Deep reinforcement learning">Deep reinforcement learning</a> has been used to approximate the value of possible <a href="/wiki/Direct_marketing" title="Direct marketing">direct marketing</a> actions, defined in terms of <a href="/wiki/RFM_(customer_value)" class="mw-redirect" title="RFM (customer value)">RFM</a> variables. The estimated value function was shown to have a natural interpretation as <a href="/wiki/Customer_lifetime_value" title="Customer lifetime value">customer lifetime value</a>.<a href="#cite_note-220">[220]</a> <div class="mw-heading mw-heading3"><h3 id="Recommendation_systems">Recommendation systems</h3>[<a href="/w/index.php?title=Deep_learning&action=edit&section=19" title="Edit section: Recommendation systems">edit</a>]</div> <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1236090951" /><div role="note" class="hatnote navigation-not-searchable">Main article: <a href="/wiki/Recommender_system" title="Recommender system">Recommender system</a></div> Recommendation systems have used deep learning to extract meaningful features for a latent factor model for content-based music and journal recommendations.<a href="#cite_note-221">[221]</a><a href="#cite_note-222">[222]</a> Multi-view deep learning has been applied for learning user preferences from multiple domains.<a href="#cite_note-223">[223]</a> The model uses a hybrid collaborative and content-based approach and enhances recommendations in multiple tasks. <div class="mw-heading mw-heading3"><h3 id="Bioinformatics">Bioinformatics</h3>[<a href="/w/index.php?title=Deep_learning&action=edit&section=20" title="Edit section: Bioinformatics">edit</a>]</div> <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1236090951" /><div role="note" class="hatnote navigation-not-searchable">Main article: <a href="/wiki/Bioinformatics" title="Bioinformatics">Bioinformatics</a></div> An <a href="/wiki/Autoencoder" title="Autoencoder">autoencoder</a> ANN was used in <a href="/wiki/Bioinformatics" title="Bioinformatics">bioinformatics</a>, to predict <a href="/wiki/Gene_Ontology" title="Gene Ontology">gene ontology</a> annotations and gene-function relationships.<a href="#cite_note-224">[224]</a> In medical informatics, deep learning was used to predict sleep quality based on data from wearables<a href="#cite_note-225">[225]</a> and predictions of health complications from <a href="/wiki/Electronic_health_record" title="Electronic health record">electronic health record</a> data.<a href="#cite_note-226">[226]</a> Deep neural networks have shown unparalleled performance in <a href="/wiki/Protein_structure_prediction" title="Protein structure prediction">predicting protein structure</a>, according to the sequence of the amino acids that make it up. In 2020, <a href="/wiki/AlphaFold" title="AlphaFold">AlphaFold</a>, a deep-learning based system, achieved a level of accuracy significantly higher than all previous computational methods.<a href="#cite_note-227">[227]</a><a href="#cite_note-228">[228]</a> <div class="mw-heading mw-heading3"><h3 id="Deep_Neural_Network_Estimations">Deep Neural Network Estimations</h3>[<a href="/w/index.php?title=Deep_learning&action=edit&section=21" title="Edit section: Deep Neural Network Estimations">edit</a>]</div> Deep neural networks can be used to estimate the entropy of a <a href="/wiki/Stochastic_process" title="Stochastic process">stochastic process</a> and called Neural Joint Entropy Estimator (NJEE).<a href="#cite_note-SPB22-229">[229]</a> Such an estimation provides insights on the effects of input <a href="/wiki/Random_variables" class="mw-redirect" title="Random variables">random variables</a> on an independent <a href="/wiki/Random_variable" title="Random variable">random variable</a>. Practically, the DNN is trained as a <a href="/wiki/Classifier_(machine_learning)" class="mw-redirect" title="Classifier (machine learning)">classifier</a> that maps an input <a href="/wiki/Vector_(mathematics_and_physics)" title="Vector (mathematics and physics)">vector</a> or <a href="/wiki/Matrix_(mathematics)" title="Matrix (mathematics)">matrix</a> X to an output <a href="/wiki/Probability_distribution" title="Probability distribution">probability distribution</a> over the possible classes of random variable Y, given input X. For example, in <a href="/wiki/Image_classification" class="mw-redirect" title="Image classification">image classification</a> tasks, the NJEE maps a vector of <a href="/wiki/Pixels" class="mw-redirect" title="Pixels">pixels</a>' color values to probabilities over possible image classes. In practice, the probability distribution of Y is obtained by a <a href="/wiki/Softmax" class="mw-redirect" title="Softmax">Softmax</a> layer with number of nodes that is equal to the <a href="/wiki/Alphabet" title="Alphabet">alphabet</a> size of Y. NJEE uses continuously differentiable <a href="/wiki/Activation_function" title="Activation function">activation functions</a>, such that the conditions for the <a href="/wiki/Universal_approximation_theorem" title="Universal approximation theorem">universal approximation theorem</a> holds. It is shown that this method provides a strongly <a href="/wiki/Consistent_estimator" title="Consistent estimator">consistent estimator</a> and outperforms other methods in case of large alphabet sizes.<a href="#cite_note-SPB22-229">[229]</a> <div class="mw-heading mw-heading3"><h3 id="Medical_image_analysis">Medical image analysis</h3>[<a href="/w/index.php?title=Deep_learning&action=edit&section=22" title="Edit section: Medical image analysis">edit</a>]</div> Deep learning has been shown to produce competitive results in medical application such as cancer cell classification, lesion detection, organ segmentation and image enhancement.<a href="#cite_note-230">[230]</a><a href="#cite_note-231">[231]</a> Modern deep learning tools demonstrate the high accuracy of detecting various diseases and the helpfulness of their use by specialists to improve the diagnosis efficiency.<a href="#cite_note-232">[232]</a><a href="#cite_note-233">[233]</a> <div class="mw-heading mw-heading3"><h3 id="Mobile_advertising">Mobile advertising</h3>[<a href="/w/index.php?title=Deep_learning&action=edit&section=23" title="Edit section: Mobile advertising">edit</a>]</div> Finding the appropriate mobile audience for <a href="/wiki/Mobile_advertising" title="Mobile advertising">mobile advertising</a> is always challenging, since many data points must be considered and analyzed before a target segment can be created and used in ad serving by any ad server.<a href="#cite_note-234">[234]</a> Deep learning has been used to interpret large, many-dimensioned advertising datasets. Many data points are collected during the request/serve/click internet advertising cycle. This information can form the basis of machine learning to improve ad selection. <div class="mw-heading mw-heading3"><h3 id="Image_restoration">Image restoration</h3>[<a href="/w/index.php?title=Deep_learning&action=edit&section=24" title="Edit section: Image restoration">edit</a>]</div> Deep learning has been successfully applied to <a href="/wiki/Inverse_problems" class="mw-redirect" title="Inverse problems">inverse problems</a> such as <a href="/wiki/Denoising" class="mw-redirect" title="Denoising">denoising</a>, <a href="/wiki/Super-resolution" class="mw-redirect" title="Super-resolution">super-resolution</a>, <a href="/wiki/Inpainting" title="Inpainting">inpainting</a>, and <a href="/wiki/Film_colorization" title="Film colorization">film colorization</a>.<a href="#cite_note-235">[235]</a> These applications include learning methods such as "Shrinkage Fields for Effective Image Restoration"<a href="#cite_note-236">[236]</a> which trains on an image dataset, and <a href="/wiki/Deep_Image_Prior" class="mw-redirect" title="Deep Image Prior">Deep Image Prior</a>, which trains on the image that needs restoration. <div class="mw-heading mw-heading3"><h3 id="Financial_fraud_detection">Financial fraud detection</h3>[<a href="/w/index.php?title=Deep_learning&action=edit&section=25" title="Edit section: Financial fraud detection">edit</a>]</div> Deep learning is being successfully applied to financial <a href="/wiki/Fraud_detection" class="mw-redirect" title="Fraud detection">fraud detection</a>, tax evasion detection,<a href="#cite_note-237">[237]</a> and anti-money laundering.<a href="#cite_note-238">[238]</a> <div class="mw-heading mw-heading3"><h3 id="Materials_science">Materials science</h3>[<a href="/w/index.php?title=Deep_learning&action=edit&section=26" title="Edit section: Materials science">edit</a>]</div> In November 2023, researchers at <a href="/wiki/Google_DeepMind" title="Google DeepMind">Google DeepMind</a> and <a href="/wiki/Lawrence_Berkeley_National_Laboratory" title="Lawrence Berkeley National Laboratory">Lawrence Berkeley National Laboratory</a> announced that they had developed an AI system known as GNoME. This system has contributed to <a href="/wiki/Materials_science" title="Materials science">materials science</a> by discovering over 2 million new materials within a relatively short timeframe. GNoME employs deep learning techniques to efficiently explore potential material structures, achieving a significant increase in the identification of stable inorganic <a href="/wiki/Crystal_structure" title="Crystal structure">crystal structures</a>. The system's predictions were validated through autonomous robotic experiments, demonstrating a noteworthy success rate of 71%. The data of newly discovered materials is publicly available through the <a href="/wiki/Materials_Project" title="Materials Project">Materials Project</a> database, offering researchers the opportunity to identify materials with desired properties for various applications. This development has implications for the future of scientific discovery and the integration of AI in material science research, potentially expediting material innovation and reducing costs in product development. The use of AI and deep learning suggests the possibility of minimizing or eliminating manual lab experiments and allowing scientists to focus more on the design and analysis of unique compounds.<a href="#cite_note-239">[239]</a><a href="#cite_note-240">[240]</a><a href="#cite_note-241">[241]</a> <div class="mw-heading mw-heading3"><h3 id="Military">Military</h3>[<a href="/w/index.php?title=Deep_learning&action=edit&section=27" title="Edit section: Military">edit</a>]</div> The United States Department of Defense applied deep learning to train robots in new tasks through observation.<a href="#cite_note-:12-242">[242]</a> <div class="mw-heading mw-heading3"><h3 id="Partial_differential_equations">Partial differential equations</h3>[<a href="/w/index.php?title=Deep_learning&action=edit&section=28" title="Edit section: Partial differential equations">edit</a>]</div> Physics informed neural networks have been used to solve <a href="/wiki/Partial_differential_equation" title="Partial differential equation">partial differential equations</a> in both forward and inverse problems in a data driven manner.<a href="#cite_note-243">[243]</a> One example is the reconstructing fluid flow governed by the <a href="/wiki/Navier%E2%80%93Stokes_equations" title="Navier–Stokes equations">Navier-Stokes equations</a>. Using physics informed neural networks does not require the often expensive mesh generation that conventional <a href="/wiki/Computational_fluid_dynamics" title="Computational fluid dynamics">CFD</a> methods rely on.<a href="#cite_note-244">[244]</a><a href="#cite_note-245">[245]</a> <div class="mw-heading mw-heading3"><h3 id="Deep_backward_stochastic_differential_equation_method">Deep backward stochastic differential equation method</h3>[<a href="/w/index.php?title=Deep_learning&action=edit&section=29" title="Edit section: Deep backward stochastic differential equation method">edit</a>]</div> <a href="/wiki/Deep_backward_stochastic_differential_equation_method" title="Deep backward stochastic differential equation method">Deep backward stochastic differential equation method</a> is a numerical method that combines deep learning with <a href="/wiki/Backward_stochastic_differential_equation" title="Backward stochastic differential equation">Backward stochastic differential equation</a> (BSDE). This method is particularly useful for solving high-dimensional problems in financial mathematics. By leveraging the powerful function approximation capabilities of <a href="/wiki/Deep_neural_networks" class="mw-redirect" title="Deep neural networks">deep neural networks</a>, deep BSDE addresses the computational challenges faced by traditional numerical methods in high-dimensional settings. Specifically, traditional methods like finite difference methods or Monte Carlo simulations often struggle with the curse of dimensionality, where computational cost increases exponentially with the number of dimensions. Deep BSDE methods, however, employ deep neural networks to approximate solutions of high-dimensional partial differential equations (PDEs), effectively reducing the computational burden.<a href="#cite_note-Han2018-246">[246]</a> In addition, the integration of <a href="/wiki/Physics-informed_neural_networks" title="Physics-informed neural networks">Physics-informed neural networks</a> (PINNs) into the deep BSDE framework enhances its capability by embedding the underlying physical laws directly into the neural network architecture. This ensures that the solutions not only fit the data but also adhere to the governing stochastic differential equations. PINNs leverage the power of deep learning while respecting the constraints imposed by the physical models, resulting in more accurate and reliable solutions for financial mathematics problems. <div class="mw-heading mw-heading3"><h3 id="Image_reconstruction">Image reconstruction</h3>[<a href="/w/index.php?title=Deep_learning&action=edit&section=30" title="Edit section: Image reconstruction">edit</a>]</div> Image reconstruction is the reconstruction of the underlying images from the image-related measurements. Several works showed the better and superior performance of the deep learning methods compared to analytical methods for various applications, e.g., spectral imaging <a href="#cite_note-247">[247]</a> and ultrasound imaging.<a href="#cite_note-248">[248]</a> <div class="mw-heading mw-heading3"><h3 id="Weather_prediction">Weather prediction</h3>[<a href="/w/index.php?title=Deep_learning&action=edit&section=31" title="Edit section: Weather prediction">edit</a>]</div> Traditional weather prediction systems solve a very complex system of partial differential equations. GraphCast is a deep learning based model, trained on a long history of weather data to predict how weather patterns change over time. It is able to predict weather conditions for up to 10 days globally, at a very detailed level, and in under a minute, with precision similar to state of the art systems.<a href="#cite_note-249">[249]</a><a href="#cite_note-250">[250]</a> <div class="mw-heading mw-heading3"><h3 id="Epigenetic_clock">Epigenetic clock</h3>[<a href="/w/index.php?title=Deep_learning&action=edit&section=32" title="Edit section: Epigenetic clock">edit</a>]</div> <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1236090951" /><div role="note" class="hatnote navigation-not-searchable">Main article: <a href="/wiki/Epigenetic_clock" title="Epigenetic clock">Epigenetic clock</a></div> An epigenetic clock is a <a href="/wiki/Biomarkers_of_aging" title="Biomarkers of aging">biochemical test</a> that can be used to measure age. Galkin et al. used deep neural networks to train an epigenetic aging clock of unprecedented accuracy using >6,000 blood samples.<a href="#cite_note-251">[251]</a> The clock uses information from 1000 <a href="/wiki/CpG_site" title="CpG site">CpG sites</a> and predicts people with certain conditions older than healthy controls: <a href="/wiki/Inflammatory_bowel_disease" title="Inflammatory bowel disease">IBD</a>, <a href="/wiki/Frontotemporal_dementia" title="Frontotemporal dementia">frontotemporal dementia</a>, <a href="/wiki/Ovarian_cancer" title="Ovarian cancer">ovarian cancer</a>, <a href="/wiki/Obesity" title="Obesity">obesity</a>. The aging clock was planned to be released for public use in 2021 by an <a href="/wiki/Insilico_Medicine" title="Insilico Medicine">Insilico Medicine</a> spinoff company Deep Longevity. <div class="mw-heading mw-heading2"><h2 id="Relation_to_human_cognitive_and_brain_development">Relation to human cognitive and brain development</h2>[<a href="/w/index.php?title=Deep_learning&action=edit&section=33" title="Edit section: Relation to human cognitive and brain development">edit</a>]</div> Deep learning is closely related to a class of theories of <a href="/wiki/Brain_development" class="mw-redirect" title="Brain development">brain development</a> (specifically, neocortical development) proposed by <a href="/wiki/Cognitive_neuroscientist" class="mw-redirect" title="Cognitive neuroscientist">cognitive neuroscientists</a> in the early 1990s.<a href="#cite_note-UTGOFF-252">[252]</a><a href="#cite_note-ELMAN-253">[253]</a><a href="#cite_note-SHRAGER-254">[254]</a><a href="#cite_note-QUARTZ-255">[255]</a> These developmental theories were instantiated in computational models, making them predecessors of deep learning systems. These developmental models share the property that various proposed learning dynamics in the brain (e.g., a wave of <a href="/wiki/Nerve_growth_factor" title="Nerve growth factor">nerve growth factor</a>) support the <a href="/wiki/Self-organization" title="Self-organization">self-organization</a> somewhat analogous to the neural networks utilized in deep learning models. Like the <a href="/wiki/Neocortex" title="Neocortex">neocortex</a>, neural networks employ a hierarchy of layered filters in which each layer considers information from a prior layer (or the operating environment), and then passes its output (and possibly the original input), to other layers. This process yields a self-organizing stack of <a href="/wiki/Transducer" title="Transducer">transducers</a>, well-tuned to their operating environment. A 1995 description stated, "...the infant's brain seems to organize itself under the influence of waves of so-called trophic-factors ... different regions of the brain become connected sequentially, with one layer of tissue maturing before another and so on until the whole brain is mature".<a href="#cite_note-BLAKESLEE-256">[256]</a> A variety of approaches have been used to investigate the plausibility of deep learning models from a neurobiological perspective. On the one hand, several variants of the <a href="/wiki/Backpropagation" title="Backpropagation">backpropagation</a> algorithm have been proposed in order to increase its processing realism.<a href="#cite_note-257">[257]</a><a href="#cite_note-258">[258]</a> Other researchers have argued that unsupervised forms of deep learning, such as those based on hierarchical <a href="/wiki/Generative_model" title="Generative model">generative models</a> and <a href="/wiki/Deep_belief_network" title="Deep belief network">deep belief networks</a>, may be closer to biological reality.<a href="#cite_note-259">[259]</a><a href="#cite_note-260">[260]</a> In this respect, generative neural network models have been related to neurobiological evidence about sampling-based processing in the cerebral cortex.<a href="#cite_note-261">[261]</a> Although a systematic comparison between the human brain organization and the neuronal encoding in deep networks has not yet been established, several analogies have been reported. For example, the computations performed by deep learning units could be similar to those of actual neurons<a href="#cite_note-262">[262]</a> and neural populations.<a href="#cite_note-263">[263]</a> Similarly, the representations developed by deep learning models are similar to those measured in the primate visual system<a href="#cite_note-264">[264]</a> both at the single-unit<a href="#cite_note-265">[265]</a> and at the population<a href="#cite_note-266">[266]</a> levels. <div class="mw-heading mw-heading2"><h2 id="Commercial_activity">Commercial activity</h2>[<a href="/w/index.php?title=Deep_learning&action=edit&section=34" title="Edit section: Commercial activity">edit</a>]</div> <a href="/wiki/Facebook" title="Facebook">Facebook</a>'s AI lab performs tasks such as <a href="/wiki/Automatic_image_annotation" title="Automatic image annotation">automatically tagging uploaded pictures</a> with the names of the people in them.<a href="#cite_note-METZ2013-267">[267]</a> Google's <a href="/wiki/DeepMind_Technologies" class="mw-redirect" title="DeepMind Technologies">DeepMind Technologies</a> developed a system capable of learning how to play <a href="/wiki/Atari" title="Atari">Atari</a> video games using only pixels as data input. In 2015 they demonstrated their <a href="/wiki/AlphaGo" title="AlphaGo">AlphaGo</a> system, which learned the game of <a href="/wiki/Go_(game)" title="Go (game)">Go</a> well enough to beat a professional Go player.<a href="#cite_note-268">[268]</a><a href="#cite_note-269">[269]</a><a href="#cite_note-270">[270]</a> <a href="/wiki/Google_Translate" title="Google Translate">Google Translate</a> uses a neural network to translate between more than 100 languages. In 2017, Covariant.ai was launched, which focuses on integrating deep learning into factories.<a href="#cite_note-271">[271]</a> As of 2008,<a href="#cite_note-272">[272]</a> researchers at <a href="/wiki/University_of_Texas_at_Austin" title="University of Texas at Austin">The University of Texas at Austin</a> (UT) developed a machine learning framework called Training an Agent Manually via Evaluative Reinforcement, or TAMER, which proposed new methods for robots or computer programs to learn how to perform tasks by interacting with a human instructor.<a href="#cite_note-:12-242">[242]</a> First developed as TAMER, a new algorithm called Deep TAMER was later introduced in 2018 during a collaboration between <a href="/wiki/U.S._Army_Research_Laboratory" class="mw-redirect" title="U.S. Army Research Laboratory">U.S. Army Research Laboratory</a> (ARL) and UT researchers. Deep TAMER used deep learning to provide a robot with the ability to learn new tasks through observation.<a href="#cite_note-:12-242">[242]</a> Using Deep TAMER, a robot learned a task with a human trainer, watching video streams or observing a human perform a task in-person. The robot later practiced the task with the help of some coaching from the trainer, who provided feedback such as "good job" and "bad job".<a href="#cite_note-273">[273]</a> <div class="mw-heading mw-heading2"><h2 id="Criticism_and_comment">Criticism and comment</h2>[<a href="/w/index.php?title=Deep_learning&action=edit&section=35" title="Edit section: Criticism and comment">edit</a>]</div> Deep learning has attracted both criticism and comment, in some cases from outside the field of computer science. <div class="mw-heading mw-heading3"><h3 id="Theory">Theory</h3>[<a href="/w/index.php?title=Deep_learning&action=edit&section=36" title="Edit section: Theory">edit</a>]</div> <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1236090951" /><div role="note" class="hatnote navigation-not-searchable">See also: <a href="/wiki/Explainable_artificial_intelligence" title="Explainable artificial intelligence">Explainable artificial intelligence</a></div> A main criticism concerns the lack of theory surrounding some methods.<a href="#cite_note-274">[274]</a> Learning in the most common deep architectures is implemented using well-understood gradient descent. However, the theory surrounding other algorithms, such as contrastive divergence is less clear.[<a href="/wiki/Wikipedia:Citation_needed" title="Wikipedia:Citation needed">citation needed</a>] (e.g., Does it converge? If so, how fast? What is it approximating?) Deep learning methods are often looked at as a <a href="/wiki/Black_box" title="Black box">black box</a>, with most confirmations done empirically, rather than theoretically.<a href="#cite_note-Knight_2017-275">[275]</a> In further reference to the idea that artistic sensitivity might be inherent in relatively low levels of the cognitive hierarchy, a published series of graphic representations of the internal states of deep (20-30 layers) neural networks attempting to discern within essentially random data the images on which they were trained<a href="#cite_note-276">[276]</a> demonstrate a visual appeal: the original research notice received well over 1,000 comments, and was the subject of what was for a time the most frequently accessed article on <a href="/wiki/The_Guardian" title="The Guardian">The Guardian</a>'s<a href="#cite_note-277">[277]</a> website. <div class="mw-heading mw-heading3"><h3 id="Errors">Errors</h3>[<a href="/w/index.php?title=Deep_learning&action=edit&section=37" title="Edit section: Errors">edit</a>]</div> Some deep learning architectures display problematic behaviors,<a href="#cite_note-goertzel-278">[278]</a> such as confidently classifying unrecognizable images as belonging to a familiar category of ordinary images (2014)<a href="#cite_note-279">[279]</a> and misclassifying minuscule perturbations of correctly classified images (2013).<a href="#cite_note-280">[280]</a> <a href="/wiki/Ben_Goertzel" title="Ben Goertzel">Goertzel</a> hypothesized that these behaviors are due to limitations in their internal representations and that these limitations would inhibit integration into heterogeneous multi-component <a href="/wiki/Artificial_general_intelligence" title="Artificial general intelligence">artificial general intelligence</a> (AGI) architectures.<a href="#cite_note-goertzel-278">[278]</a> These issues may possibly be addressed by deep learning architectures that internally form states homologous to image-grammar<a href="#cite_note-281">[281]</a> decompositions of observed entities and events.<a href="#cite_note-goertzel-278">[278]</a> <a href="/wiki/Grammar_induction" title="Grammar induction">Learning a grammar</a> (visual or linguistic) from training data would be equivalent to restricting the system to <a href="/wiki/Commonsense_reasoning" title="Commonsense reasoning">commonsense reasoning</a> that operates on concepts in terms of grammatical <a href="/wiki/Production_(computer_science)" title="Production (computer science)">production rules</a> and is a basic goal of both human language acquisition<a href="#cite_note-282">[282]</a> and <a href="/wiki/Artificial_intelligence" title="Artificial intelligence">artificial intelligence</a> (AI).<a href="#cite_note-283">[283]</a> <div class="mw-heading mw-heading3"><h3 id="Cyber_threat">Cyber threat</h3>[<a href="/w/index.php?title=Deep_learning&action=edit&section=38" title="Edit section: Cyber threat">edit</a>]</div> As deep learning moves from the lab into the world, research and experience show that artificial neural networks are vulnerable to hacks and deception.<a href="#cite_note-284">[284]</a> By identifying patterns that these systems use to function, attackers can modify inputs to ANNs in such a way that the ANN finds a match that human observers would not recognize. For example, an attacker can make subtle changes to an image such that the ANN finds a match even though the image looks to a human nothing like the search target. Such manipulation is termed an "<a href="/wiki/Adversarial_machine_learning" title="Adversarial machine learning">adversarial attack</a>".<a href="#cite_note-285">[285]</a> In 2016 researchers used one ANN to doctor images in trial and error fashion, identify another's focal points, and thereby generate images that deceived it. The modified images looked no different to human eyes. Another group showed that printouts of doctored images then photographed successfully tricked an image classification system.<a href="#cite_note-:4-286">[286]</a> One defense is reverse image search, in which a possible fake image is submitted to a site such as <a href="/wiki/TinEye" title="TinEye">TinEye</a> that can then find other instances of it. A refinement is to search using only parts of the image, to identify images from which that piece may have been taken.<a href="#cite_note-287">[287]</a> Another group showed that certain <a href="/wiki/Psychedelic_art" title="Psychedelic art">psychedelic</a> spectacles could fool a <a href="/wiki/Facial_recognition_system" title="Facial recognition system">facial recognition system</a> into thinking ordinary people were celebrities, potentially allowing one person to impersonate another. In 2017 researchers added stickers to <a href="/wiki/Stop_sign" title="Stop sign">stop signs</a> and caused an ANN to misclassify them.<a href="#cite_note-:4-286">[286]</a> ANNs can however be further trained to detect attempts at <a href="/wiki/Deception" title="Deception">deception</a>, potentially leading attackers and defenders into an arms race similar to the kind that already defines the <a href="/wiki/Malware" title="Malware">malware</a> defense industry. ANNs have been trained to defeat ANN-based anti-<a href="/wiki/Malware" title="Malware">malware</a> software by repeatedly attacking a defense with malware that was continually altered by a <a href="/wiki/Genetic_algorithm" title="Genetic algorithm">genetic algorithm</a> until it tricked the anti-malware while retaining its ability to damage the target.<a href="#cite_note-:4-286">[286]</a> In 2016, another group demonstrated that certain sounds could make the <a href="/wiki/Google_Now" title="Google Now">Google Now</a> voice command system open a particular web address, and hypothesized that this could "serve as a stepping stone for further attacks (e.g., opening a web page hosting drive-by malware)".<a href="#cite_note-:4-286">[286]</a> In "<a href="/wiki/Adversarial_machine_learning#Data_poisoning" title="Adversarial machine learning">data poisoning</a>", false data is continually smuggled into a machine learning system's training set to prevent it from achieving mastery.<a href="#cite_note-:4-286">[286]</a> <div class="mw-heading mw-heading3"><h3 id="Data_collection_ethics">Data collection ethics</h3>[<a href="/w/index.php?title=Deep_learning&action=edit&section=39" title="Edit section: Data collection ethics">edit</a>]</div> The deep learning systems that are trained using supervised learning often rely on data that is created and/or annotated by humans.<a href="#cite_note-288">[288]</a> It has been argued that not only low-paid <a href="/wiki/Clickworkers" title="Clickworkers">clickwork</a> (such as on <a href="/wiki/Amazon_Mechanical_Turk" title="Amazon Mechanical Turk">Amazon Mechanical Turk</a>) is regularly deployed for this purpose, but also implicit forms of human <a href="/wiki/Microwork" title="Microwork">microwork</a> that are often not recognized as such.<a href="#cite_note-:13-289">[289]</a> The philosopher <a href="/wiki/Rainer_M%C3%BChlhoff" title="Rainer Mühlhoff">Rainer Mühlhoff</a> distinguishes five types of "machinic capture" of human microwork to generate training data: (1) <a href="/wiki/Gamification" title="Gamification">gamification</a> (the embedding of annotation or computation tasks in the flow of a game), (2) "trapping and tracking" (e.g. <a href="/wiki/CAPTCHA" title="CAPTCHA">CAPTCHAs</a> for image recognition or click-tracking on Google <a href="/wiki/Search_engine_results_page" title="Search engine results page">search results pages</a>), (3) exploitation of social motivations (e.g. <a href="/wiki/Tag_(Facebook)" class="mw-redirect" title="Tag (Facebook)">tagging faces</a> on <a href="/wiki/Facebook" title="Facebook">Facebook</a> to obtain labeled facial images), (4) <a href="/wiki/Information_mining" class="mw-redirect" title="Information mining">information mining</a> (e.g. by leveraging <a href="/wiki/Quantified_self" title="Quantified self">quantified-self</a> devices such as <a href="/wiki/Activity_tracker" class="mw-redirect" title="Activity tracker">activity trackers</a>) and (5) <a href="/wiki/Clickworkers" title="Clickworkers">clickwork</a>.<a href="#cite_note-:13-289">[289]</a> <div class="mw-heading mw-heading2"><h2 id="See_also">See also</h2>[<a href="/w/index.php?title=Deep_learning&action=edit&section=40" title="Edit section: See also">edit</a>]</div> <ul><li><a href="/wiki/Applications_of_artificial_intelligence" title="Applications of artificial intelligence">Applications of artificial intelligence</a></li> <li><a href="/wiki/Comparison_of_deep_learning_software" title="Comparison of deep learning software">Comparison of deep learning software</a></li> <li><a href="/wiki/Compressed_sensing" title="Compressed sensing">Compressed sensing</a></li> <li><a href="/wiki/Differentiable_programming" title="Differentiable programming">Differentiable programming</a></li> <li><a href="/wiki/Echo_state_network" title="Echo state network">Echo state network</a></li> <li><a href="/wiki/List_of_artificial_intelligence_projects" title="List of artificial intelligence projects">List of artificial intelligence projects</a></li> <li><a href="/wiki/Liquid_state_machine" title="Liquid state machine">Liquid state machine</a></li> <li><a href="/wiki/List_of_datasets_for_machine-learning_research" title="List of datasets for machine-learning research">List of datasets for machine-learning research</a></li> <li><a href="/wiki/Reservoir_computing" title="Reservoir computing">Reservoir computing</a></li> <li><a href="/wiki/Scale_space#Deep_learning_and_scale_space" title="Scale space">Scale space and deep learning</a></li> <li><a href="/wiki/Sparse_coding" class="mw-redirect" title="Sparse coding">Sparse coding</a></li> <li><a href="/wiki/Stochastic_parrot" title="Stochastic parrot">Stochastic parrot</a></li> <li><a href="/wiki/Topological_deep_learning" title="Topological deep learning">Topological deep learning</a></li></ul> <div class="mw-heading mw-heading2"><h2 id="References">References</h2>[<a href="/w/index.php?title=Deep_learning&action=edit&section=41" title="Edit section: References">edit</a>]</div> <style data-mw-deduplicate="TemplateStyles:r1239543626">.mw-parser-output .reflist{margin-bottom:0.5em;list-style-type:decimal}@media screen{.mw-parser-output .reflist{font-size:90%}}.mw-parser-output .reflist .references{font-size:100%;margin-bottom:0;list-style-type:inherit}.mw-parser-output .reflist-columns-2{column-width:30em}.mw-parser-output .reflist-columns-3{column-width:25em}.mw-parser-output .reflist-columns{margin-top:0.3em}.mw-parser-output .reflist-columns ol{margin-top:0}.mw-parser-output .reflist-columns li{page-break-inside:avoid;break-inside:avoid-column}.mw-parser-output .reflist-upper-alpha{list-style-type:upper-alpha}.mw-parser-output .reflist-upper-roman{list-style-type:upper-roman}.mw-parser-output .reflist-lower-alpha{list-style-type:lower-alpha}.mw-parser-output .reflist-lower-greek{list-style-type:lower-greek}.mw-parser-output .reflist-lower-roman{list-style-type:lower-roman}</style><div class="reflist reflist-columns references-column-width" style="column-width: 30em;"> <ol class="references"> <li id="cite_note-1"><a href="#cite_ref-1">^</a> <style data-mw-deduplicate="TemplateStyles:r1238218222">.mw-parser-output cite.citation{font-style:inherit;word-wrap:break-word}.mw-parser-output .citation q{quotes:"\"""\"""'""'"}.mw-parser-output .citation:target{background-color:rgba(0,127,255,0.133)}.mw-parser-output .id-lock-free.id-lock-free a{background:url("//upload.wikimedia.org/wikipedia/commons/6/65/Lock-green.svg")right 0.1em center/9px no-repeat}.mw-parser-output .id-lock-limited.id-lock-limited a,.mw-parser-output .id-lock-registration.id-lock-registration a{background:url("//upload.wikimedia.org/wikipedia/commons/d/d6/Lock-gray-alt-2.svg")right 0.1em center/9px no-repeat}.mw-parser-output .id-lock-subscription.id-lock-subscription 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Retrieved 11 October 2019.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=unknown&rft.jtitle=The+Daily+Dot&rft.atitle=How+hackers+can+force+AI+to+make+dumb+mistakes&rft.date=2018-06-18&rft_id=https%3A%2F%2Fwww.dailydot.com%2Fdebug%2Fadversarial-attacks-ai-mistakes%2F&rfr_id=info%3Asid%2Fen.wikipedia.org%3ADeep+learning" class="Z3988"> </li> <li id="cite_note-:4-286">^ <a href="#cite_ref-:4_286-0">a</a> <a href="#cite_ref-:4_286-1">b</a> <a href="#cite_ref-:4_286-2">c</a> <a href="#cite_ref-:4_286-3">d</a> <a href="#cite_ref-:4_286-4">e</a> <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite class="citation news cs1"><a rel="nofollow" class="external text" href="https://singularityhub.com/2017/10/10/ai-is-easy-to-fool-why-that-needs-to-change">"AI Is Easy to Fool—Why That Needs to Change"</a>. Singularity Hub. 10 October 2017. <a rel="nofollow" class="external text" href="https://web.archive.org/web/20171011233017/https://singularityhub.com/2017/10/10/ai-is-easy-to-fool-why-that-needs-to-change/">Archived</a> from the original on 11 October 2017. Retrieved 11 October 2017.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Singularity+Hub&rft.atitle=AI+Is+Easy+to+Fool%E2%80%94Why+That+Needs+to+Change&rft.date=2017-10-10&rft_id=https%3A%2F%2Fsingularityhub.com%2F2017%2F10%2F10%2Fai-is-easy-to-fool-why-that-needs-to-change&rfr_id=info%3Asid%2Fen.wikipedia.org%3ADeep+learning" class="Z3988"> </li> <li id="cite_note-287"><a href="#cite_ref-287">^</a> <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite id="CITEREFGibney2017" class="citation journal cs1">Gibney, Elizabeth (2017). <a rel="nofollow" class="external text" href="https://www.nature.com/news/the-scientist-who-spots-fake-videos-1.22784">"The scientist who spots fake videos"</a>. Nature. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1038%2Fnature.2017.22784">10.1038/nature.2017.22784</a>. <a rel="nofollow" class="external text" href="https://web.archive.org/web/20171010011017/http://www.nature.com/news/the-scientist-who-spots-fake-videos-1.22784">Archived</a> from the original on 2017-10-10. Retrieved 2017-10-11.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Nature&rft.atitle=The+scientist+who+spots+fake+videos&rft.date=2017&rft_id=info%3Adoi%2F10.1038%2Fnature.2017.22784&rft.aulast=Gibney&rft.aufirst=Elizabeth&rft_id=https%3A%2F%2Fwww.nature.com%2Fnews%2Fthe-scientist-who-spots-fake-videos-1.22784&rfr_id=info%3Asid%2Fen.wikipedia.org%3ADeep+learning" class="Z3988"> </li> <li id="cite_note-288"><a href="#cite_ref-288">^</a> <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite id="CITEREFTubaro2020" class="citation journal cs1">Tubaro, Paola (2020). <a rel="nofollow" class="external text" href="https://hal.science/hal-03029735">"Whose intelligence is artificial intelligence?"</a>. Global Dialogue: 38–39.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Global+Dialogue&rft.atitle=Whose+intelligence+is+artificial+intelligence%3F&rft.pages=%3Cspan+class%3D%22nowrap%22%3E38-%3C%2Fspan%3E39&rft.date=2020&rft.aulast=Tubaro&rft.aufirst=Paola&rft_id=https%3A%2F%2Fhal.science%2Fhal-03029735&rfr_id=info%3Asid%2Fen.wikipedia.org%3ADeep+learning" class="Z3988"> </li> <li id="cite_note-:13-289">^ <a href="#cite_ref-:13_289-0">a</a> <a href="#cite_ref-:13_289-1">b</a> <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite id="CITEREFMühlhoff2019" class="citation journal cs1">Mühlhoff, Rainer (6 November 2019). <a rel="nofollow" class="external text" href="https://depositonce.tu-berlin.de/handle/11303/12510">"Human-aided artificial intelligence: Or, how to run large computations in human brains? Toward a media sociology of machine learning"</a>. New Media & Society. 22 (10): 1868–1884. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1177%2F1461444819885334">10.1177/1461444819885334</a>. <a href="/wiki/ISSN_(identifier)" class="mw-redirect" title="ISSN (identifier)">ISSN</a> <a rel="nofollow" class="external text" href="https://search.worldcat.org/issn/1461-4448">1461-4448</a>. <a href="/wiki/S2CID_(identifier)" class="mw-redirect" title="S2CID (identifier)">S2CID</a> <a rel="nofollow" class="external text" href="https://api.semanticscholar.org/CorpusID:209363848">209363848</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=New+Media+%26+Society&rft.atitle=Human-aided+artificial+intelligence%3A+Or%2C+how+to+run+large+computations+in+human+brains%3F+Toward+a+media+sociology+of+machine+learning&rft.volume=22&rft.issue=10&rft.pages=%3Cspan+class%3D%22nowrap%22%3E1868-%3C%2Fspan%3E1884&rft.date=2019-11-06&rft_id=https%3A%2F%2Fapi.semanticscholar.org%2FCorpusID%3A209363848%23id-name%3DS2CID&rft.issn=1461-4448&rft_id=info%3Adoi%2F10.1177%2F1461444819885334&rft.aulast=M%C3%BChlhoff&rft.aufirst=Rainer&rft_id=https%3A%2F%2Fdepositonce.tu-berlin.de%2Fhandle%2F11303%2F12510&rfr_id=info%3Asid%2Fen.wikipedia.org%3ADeep+learning" class="Z3988"> </li> </ol></div> <div class="mw-heading mw-heading2"><h2 id="Further_reading">Further reading</h2>[<a href="/w/index.php?title=Deep_learning&action=edit&section=42" title="Edit section: Further reading">edit</a>]</div> <style data-mw-deduplicate="TemplateStyles:r1239549316">.mw-parser-output .refbegin{margin-bottom:0.5em}.mw-parser-output .refbegin-hanging-indents>ul{margin-left:0}.mw-parser-output .refbegin-hanging-indents>ul>li{margin-left:0;padding-left:3.2em;text-indent:-3.2em}.mw-parser-output .refbegin-hanging-indents ul,.mw-parser-output .refbegin-hanging-indents ul li{list-style:none}@media(max-width:720px){.mw-parser-output .refbegin-hanging-indents>ul>li{padding-left:1.6em;text-indent:-1.6em}}.mw-parser-output .refbegin-columns{margin-top:0.3em}.mw-parser-output .refbegin-columns ul{margin-top:0}.mw-parser-output .refbegin-columns li{page-break-inside:avoid;break-inside:avoid-column}@media screen{.mw-parser-output .refbegin{font-size:90%}}</style><div class="refbegin" style=""> <ul><li><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite id="CITEREFBishopBishop2024" class="citation book cs1">Bishop, Christopher M.; Bishop, Hugh (2024). Deep learning: foundations and concepts. Springer. <a href="/wiki/ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <a href="/wiki/Special:BookSources/978-3-031-45467-7" title="Special:BookSources/978-3-031-45467-7"><bdi>978-3-031-45467-7</bdi></a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=book&rft.btitle=Deep+learning%3A+foundations+and+concepts&rft.pub=Springer&rft.date=2024&rft.isbn=978-3-031-45467-7&rft.aulast=Bishop&rft.aufirst=Christopher+M.&rft.au=Bishop%2C+Hugh&rfr_id=info%3Asid%2Fen.wikipedia.org%3ADeep+learning" class="Z3988"></li> <li><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite id="CITEREFPrince2023" class="citation book cs1">Prince, Simon J. D. (2023). Understanding deep learning. The MIT Press. <a href="/wiki/ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <a href="/wiki/Special:BookSources/9780262048644" title="Special:BookSources/9780262048644"><bdi>9780262048644</bdi></a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=book&rft.btitle=Understanding+deep+learning&rft.pub=The+MIT+Press&rft.date=2023&rft.isbn=9780262048644&rft.aulast=Prince&rft.aufirst=Simon+J.+D.&rfr_id=info%3Asid%2Fen.wikipedia.org%3ADeep+learning" class="Z3988"></li> <li><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite id="CITEREFGoodfellowBengioCourville2016" class="citation book cs1"><a href="/wiki/Ian_Goodfellow" title="Ian Goodfellow">Goodfellow, Ian</a>; <a href="/wiki/Yoshua_Bengio" title="Yoshua Bengio">Bengio, Yoshua</a>; Courville, Aaron (2016). <a rel="nofollow" class="external text" href="http://www.deeplearningbook.org">Deep Learning</a>. MIT Press. <a href="/wiki/ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <a href="/wiki/Special:BookSources/978-0-26203561-3" title="Special:BookSources/978-0-26203561-3"><bdi>978-0-26203561-3</bdi></a>. <a rel="nofollow" class="external text" href="https://web.archive.org/web/20160416111010/http://www.deeplearningbook.org/">Archived</a> from the original on 2016-04-16. Retrieved 2021-05-09, introductory textbook.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=book&rft.btitle=Deep+Learning&rft.pub=MIT+Press&rft.date=2016&rft.isbn=978-0-26203561-3&rft.aulast=Goodfellow&rft.aufirst=Ian&rft.au=Bengio%2C+Yoshua&rft.au=Courville%2C+Aaron&rft_id=http%3A%2F%2Fwww.deeplearningbook.org&rfr_id=info%3Asid%2Fen.wikipedia.org%3ADeep+learning" class="Z3988"><code class="cs1-code">{{<a href="/wiki/Template:Cite_book" title="Template:Cite book">cite book</a>}}</code>: CS1 maint: postscript (<a href="/wiki/Category:CS1_maint:_postscript" title="Category:CS1 maint: postscript">link</a>)</li></ul> </div> <div class="navbox-styles"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1129693374" /><style data-mw-deduplicate="TemplateStyles:r1236075235">.mw-parser-output .navbox{box-sizing:border-box;border:1px solid #a2a9b1;width:100%;clear:both;font-size:88%;text-align:center;padding:1px;margin:1em auto 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.navbox-abovebelow,.mw-parser-output .navbox-group,.mw-parser-output .navbox-subgroup .navbox-title{background-color:#ddf}.mw-parser-output .navbox-subgroup .navbox-group,.mw-parser-output .navbox-subgroup .navbox-abovebelow{background-color:#e6e6ff}.mw-parser-output .navbox-even{background-color:#f7f7f7}.mw-parser-output .navbox-odd{background-color:transparent}.mw-parser-output .navbox .hlist td dl,.mw-parser-output .navbox .hlist td ol,.mw-parser-output .navbox .hlist td ul,.mw-parser-output .navbox td.hlist dl,.mw-parser-output .navbox td.hlist ol,.mw-parser-output .navbox td.hlist ul{padding:0.125em 0}.mw-parser-output .navbox .navbar{display:block;font-size:100%}.mw-parser-output .navbox-title .navbar{float:left;text-align:left;margin-right:0.5em}body.skin--responsive .mw-parser-output .navbox-image img{max-width:none!important}@media print{body.ns-0 .mw-parser-output .navbox{display:none!important}}</style></div><div role="navigation" class="navbox" aria-labelledby="Artificial_intelligence_(AI)752" style="padding:3px"><table class="nowraplinks hlist mw-collapsible autocollapse navbox-inner" style="border-spacing:0;background:transparent;color:inherit"><tbody><tr><th scope="col" class="navbox-title" colspan="2"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1129693374" /><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1239400231" /><div class="navbar plainlinks hlist navbar-mini"><ul><li class="nv-view"><a href="/wiki/Template:Artificial_intelligence_navbox" title="Template:Artificial intelligence navbox"><abbr title="View this template">v</abbr></a></li><li class="nv-talk"><a href="/wiki/Template_talk:Artificial_intelligence_navbox" title="Template talk:Artificial intelligence navbox"><abbr title="Discuss this template">t</abbr></a></li><li class="nv-edit"><a href="/wiki/Special:EditPage/Template:Artificial_intelligence_navbox" title="Special:EditPage/Template:Artificial intelligence navbox"><abbr title="Edit this template">e</abbr></a></li></ul></div><div id="Artificial_intelligence_(AI)752" style="font-size:114%;margin:0 4em"><a href="/wiki/Artificial_intelligence" title="Artificial intelligence">Artificial intelligence</a> (AI)</div></th></tr><tr><td class="navbox-abovebelow" colspan="2"><div><a href="/wiki/History_of_artificial_intelligence" title="History of artificial intelligence">History</a> (<a href="/wiki/Timeline_of_artificial_intelligence" title="Timeline of artificial intelligence">timeline</a>)</div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Concepts</th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/Parameter" title="Parameter">Parameter</a> <ul><li><a href="/wiki/Hyperparameter_(machine_learning)" title="Hyperparameter (machine learning)">Hyperparameter</a></li></ul></li> <li><a href="/wiki/Loss_functions_for_classification" title="Loss functions for classification">Loss functions</a></li> <li><a href="/wiki/Regression_analysis" title="Regression analysis">Regression</a> <ul><li><a href="/wiki/Bias%E2%80%93variance_tradeoff" title="Bias–variance tradeoff">Bias–variance tradeoff</a></li> <li><a href="/wiki/Double_descent" title="Double descent">Double descent</a></li> <li><a href="/wiki/Overfitting" title="Overfitting">Overfitting</a></li></ul></li> <li><a href="/wiki/Cluster_analysis" title="Cluster analysis">Clustering</a></li> <li><a href="/wiki/Gradient_descent" title="Gradient descent">Gradient descent</a> <ul><li><a href="/wiki/Stochastic_gradient_descent" title="Stochastic gradient descent">SGD</a></li> <li><a href="/wiki/Quasi-Newton_method" title="Quasi-Newton method">Quasi-Newton method</a></li> <li><a href="/wiki/Conjugate_gradient_method" title="Conjugate gradient method">Conjugate gradient method</a></li></ul></li> <li><a href="/wiki/Backpropagation" title="Backpropagation">Backpropagation</a></li> <li><a href="/wiki/Attention_(machine_learning)" title="Attention (machine learning)">Attention</a></li> <li><a href="/wiki/Convolution" title="Convolution">Convolution</a></li> <li><a href="/wiki/Normalization_(machine_learning)" title="Normalization (machine learning)">Normalization</a> <ul><li><a href="/wiki/Batch_normalization" title="Batch normalization">Batchnorm</a></li></ul></li> <li><a href="/wiki/Activation_function" title="Activation function">Activation</a> <ul><li><a href="/wiki/Softmax_function" title="Softmax function">Softmax</a></li> <li><a href="/wiki/Sigmoid_function" title="Sigmoid function">Sigmoid</a></li> <li><a href="/wiki/Rectifier_(neural_networks)" title="Rectifier (neural networks)">Rectifier</a></li></ul></li> <li><a href="/wiki/Gating_mechanism" title="Gating mechanism">Gating</a></li> <li><a href="/wiki/Weight_initialization" title="Weight initialization">Weight initialization</a></li> <li><a href="/wiki/Regularization_(mathematics)" title="Regularization (mathematics)">Regularization</a></li> <li><a href="/wiki/Training,_validation,_and_test_data_sets" title="Training, validation, and test data sets">Datasets</a> <ul><li><a href="/wiki/Data_augmentation" title="Data augmentation">Augmentation</a></li></ul></li> <li><a href="/wiki/Prompt_engineering" title="Prompt engineering">Prompt engineering</a></li> <li><a href="/wiki/Reinforcement_learning" title="Reinforcement learning">Reinforcement learning</a> <ul><li><a href="/wiki/Q-learning" title="Q-learning">Q-learning</a></li> <li><a href="/wiki/State%E2%80%93action%E2%80%93reward%E2%80%93state%E2%80%93action" title="State–action–reward–state–action">SARSA</a></li> <li><a href="/wiki/Imitation_learning" title="Imitation learning">Imitation</a></li> <li><a href="/wiki/Policy_gradient_method" title="Policy gradient method">Policy gradient</a></li></ul></li> <li><a href="/wiki/Diffusion_process" title="Diffusion process">Diffusion</a></li> <li><a href="/wiki/Latent_diffusion_model" title="Latent diffusion model">Latent diffusion model</a></li> <li><a href="/wiki/Autoregressive_model" title="Autoregressive model">Autoregression</a></li> <li><a href="/wiki/Adversarial_machine_learning" title="Adversarial machine learning">Adversary</a></li> <li><a href="/wiki/Retrieval-augmented_generation" title="Retrieval-augmented generation">RAG</a></li> <li><a href="/wiki/Uncanny_valley" title="Uncanny valley">Uncanny valley</a></li> <li><a href="/wiki/Reinforcement_learning_from_human_feedback" title="Reinforcement learning from human feedback">RLHF</a></li> <li><a href="/wiki/Self-supervised_learning" title="Self-supervised learning">Self-supervised learning</a></li> <li><a href="/wiki/Recursive_self-improvement" title="Recursive self-improvement">Recursive self-improvement</a></li> <li><a href="/wiki/Word_embedding" title="Word embedding">Word embedding</a></li> <li><a href="/wiki/Hallucination_(artificial_intelligence)" title="Hallucination (artificial intelligence)">Hallucination</a></li></ul> </div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Applications</th><td class="navbox-list-with-group navbox-list navbox-even" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/Machine_learning" title="Machine learning">Machine learning</a> <ul><li><a href="/wiki/Prompt_engineering#In-context_learning" title="Prompt engineering">In-context learning</a></li></ul></li> <li><a href="/wiki/Neural_network_(machine_learning)" title="Neural network (machine learning)">Artificial neural network</a> <ul><li><a class="mw-selflink selflink">Deep learning</a></li></ul></li> <li><a href="/wiki/Language_model" title="Language model">Language model</a> <ul><li><a href="/wiki/Large_language_model" title="Large language model">Large language model</a></li> <li><a href="/wiki/Neural_machine_translation" title="Neural machine translation">NMT</a></li></ul></li> <li><a href="/wiki/Artificial_general_intelligence" title="Artificial general intelligence">Artificial general intelligence</a></li></ul> </div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Implementations</th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em"></div><table class="nowraplinks navbox-subgroup" style="border-spacing:0"><tbody><tr><th scope="row" class="navbox-group" style="width:1%">Audio–visual</th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/AlexNet" title="AlexNet">AlexNet</a></li> <li><a href="/wiki/WaveNet" title="WaveNet">WaveNet</a></li> <li><a href="/wiki/Human_image_synthesis" title="Human image synthesis">Human image synthesis</a></li> <li><a href="/wiki/Handwriting_recognition" title="Handwriting recognition">HWR</a></li> <li><a href="/wiki/Optical_character_recognition" title="Optical character recognition">OCR</a></li> <li><a href="/wiki/Deep_learning_speech_synthesis" title="Deep learning speech synthesis">Speech synthesis</a> <ul><li><a href="/wiki/15.ai" title="15.ai">15.ai</a></li> <li><a href="/wiki/ElevenLabs" title="ElevenLabs">ElevenLabs</a></li></ul></li> <li><a href="/wiki/Speech_recognition" title="Speech recognition">Speech recognition</a> <ul><li><a href="/wiki/Whisper_(speech_recognition_system)" title="Whisper (speech recognition system)">Whisper</a></li></ul></li> <li><a href="/wiki/Facial_recognition_system" title="Facial recognition system">Facial recognition</a></li> <li><a href="/wiki/AlphaFold" title="AlphaFold">AlphaFold</a></li> <li><a href="/wiki/Text-to-image_model" title="Text-to-image model">Text-to-image models</a> <ul><li><a href="/wiki/Aurora_(text-to-image_model)" class="mw-redirect" title="Aurora (text-to-image model)">Aurora</a></li> <li><a href="/wiki/DALL-E" title="DALL-E">DALL-E</a></li> <li><a href="/wiki/Adobe_Firefly" title="Adobe Firefly">Firefly</a></li> <li><a href="/wiki/Flux_(text-to-image_model)" title="Flux (text-to-image model)">Flux</a></li> <li><a href="/wiki/Ideogram_(text-to-image_model)" title="Ideogram (text-to-image model)">Ideogram</a></li> <li><a href="/wiki/Google_Brain#Text-to-image_model" title="Google Brain">Imagen</a></li> <li><a href="/wiki/Midjourney" title="Midjourney">Midjourney</a></li> <li><a href="/wiki/Stable_Diffusion" title="Stable Diffusion">Stable Diffusion</a></li></ul></li> <li><a href="/wiki/Text-to-video_model" title="Text-to-video model">Text-to-video models</a> <ul><li><a href="/wiki/Dream_Machine_(text-to-video_model)" title="Dream Machine (text-to-video model)">Dream Machine</a></li> <li><a href="/wiki/Runway_(company)#Gen-3_Alpha" title="Runway (company)">Gen-3 Alpha</a></li> <li><a href="/wiki/MiniMax_(company)#Hailuo_AI" title="MiniMax (company)">Hailuo AI</a></li> <li><a href="/wiki/Kling_(text-to-video_model)" class="mw-redirect" title="Kling (text-to-video model)">Kling</a></li> <li><a href="/wiki/Sora_(text-to-video_model)" title="Sora (text-to-video model)">Sora</a></li> <li><a href="/wiki/Google_DeepMind#Video_model" title="Google DeepMind">Veo</a></li></ul></li> <li><a href="/wiki/Music_and_artificial_intelligence" title="Music and artificial intelligence">Music generation</a> <ul><li><a href="/wiki/Suno_AI" title="Suno AI">Suno AI</a></li> <li><a href="/wiki/Udio" title="Udio">Udio</a></li></ul></li></ul> </div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Text</th><td class="navbox-list-with-group navbox-list navbox-even" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/Word2vec" title="Word2vec">Word2vec</a></li> <li><a href="/wiki/Seq2seq" title="Seq2seq">Seq2seq</a></li> <li><a href="/wiki/GloVe" title="GloVe">GloVe</a></li> <li><a href="/wiki/BERT_(language_model)" title="BERT (language model)">BERT</a></li> <li><a href="/wiki/T5_(language_model)" title="T5 (language model)">T5</a></li> <li><a href="/wiki/Llama_(language_model)" title="Llama (language model)">Llama</a></li> <li><a href="/wiki/Chinchilla_(language_model)" title="Chinchilla (language model)">Chinchilla AI</a></li> <li><a href="/wiki/PaLM" title="PaLM">PaLM</a></li> <li><a href="/wiki/Generative_pre-trained_transformer" title="Generative pre-trained transformer">GPT</a> <ul><li><a href="/wiki/GPT-1" title="GPT-1">1</a></li> <li><a href="/wiki/GPT-2" title="GPT-2">2</a></li> <li><a href="/wiki/GPT-3" title="GPT-3">3</a></li> <li><a href="/wiki/GPT-J" title="GPT-J">J</a></li> <li><a href="/wiki/ChatGPT" title="ChatGPT">ChatGPT</a></li> <li><a href="/wiki/GPT-4" title="GPT-4">4</a></li> <li><a href="/wiki/GPT-4o" title="GPT-4o">4o</a></li> <li><a href="/wiki/GPT-4.5" title="GPT-4.5">4.5</a></li> <li><a href="/wiki/OpenAI_o1" title="OpenAI o1">o1</a></li> <li><a href="/wiki/OpenAI_o3" title="OpenAI o3">o3</a></li></ul></li> <li><a href="/wiki/Claude_(language_model)" title="Claude (language model)">Claude</a></li> <li><a href="/wiki/Gemini_(language_model)" title="Gemini (language model)">Gemini</a> <ul><li><a href="/wiki/Gemini_(chatbot)" title="Gemini (chatbot)">chatbot</a></li></ul></li> <li><a href="/wiki/Grok_(chatbot)" title="Grok (chatbot)">Grok</a></li> <li><a href="/wiki/LaMDA" title="LaMDA">LaMDA</a></li> <li><a href="/wiki/BLOOM_(language_model)" title="BLOOM (language model)">BLOOM</a></li> <li><a href="/wiki/Project_Debater" title="Project Debater">Project Debater</a></li> <li><a href="/wiki/IBM_Watson" title="IBM Watson">IBM Watson</a></li> <li><a href="/wiki/IBM_Watsonx" title="IBM Watsonx">IBM Watsonx</a></li> <li><a href="/wiki/IBM_Granite" title="IBM Granite">Granite</a></li> <li><a href="/wiki/Huawei_PanGu" title="Huawei PanGu">PanGu-Σ</a></li> <li><a href="/wiki/DeepSeek_(chatbot)" title="DeepSeek (chatbot)">DeepSeek</a></li> <li><a href="/wiki/Qwen" title="Qwen">Qwen</a></li></ul> </div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Decisional</th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/AlphaGo" title="AlphaGo">AlphaGo</a></li> <li><a href="/wiki/AlphaZero" title="AlphaZero">AlphaZero</a></li> <li><a href="/wiki/OpenAI_Five" title="OpenAI Five">OpenAI Five</a></li> <li><a href="/wiki/Self-driving_car" title="Self-driving car">Self-driving car</a></li> <li><a href="/wiki/MuZero" title="MuZero">MuZero</a></li> <li><a href="/wiki/Action_selection" title="Action selection">Action selection</a> <ul><li><a href="/wiki/AutoGPT" title="AutoGPT">AutoGPT</a></li></ul></li> <li><a href="/wiki/Robot_control" title="Robot control">Robot control</a></li></ul> </div></td></tr></tbody></table><div></div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">People</th><td class="navbox-list-with-group navbox-list navbox-even" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/Alan_Turing" title="Alan Turing">Alan Turing</a></li> <li><a href="/wiki/Warren_Sturgis_McCulloch" title="Warren Sturgis McCulloch">Warren Sturgis McCulloch</a></li> <li><a href="/wiki/Walter_Pitts" title="Walter Pitts">Walter Pitts</a></li> <li><a href="/wiki/John_von_Neumann" title="John von Neumann">John von Neumann</a></li> <li><a href="/wiki/Claude_Shannon" title="Claude Shannon">Claude Shannon</a></li> <li><a href="/wiki/Marvin_Minsky" title="Marvin Minsky">Marvin Minsky</a></li> <li><a href="/wiki/John_McCarthy_(computer_scientist)" title="John McCarthy (computer scientist)">John McCarthy</a></li> <li><a href="/wiki/Nathaniel_Rochester_(computer_scientist)" title="Nathaniel Rochester (computer scientist)">Nathaniel Rochester</a></li> <li><a href="/wiki/Allen_Newell" title="Allen Newell">Allen Newell</a></li> <li><a href="/wiki/Cliff_Shaw" title="Cliff Shaw">Cliff Shaw</a></li> <li><a href="/wiki/Herbert_A._Simon" title="Herbert A. Simon">Herbert A. Simon</a></li> <li><a href="/wiki/Oliver_Selfridge" title="Oliver Selfridge">Oliver Selfridge</a></li> <li><a href="/wiki/Frank_Rosenblatt" title="Frank Rosenblatt">Frank Rosenblatt</a></li> <li><a href="/wiki/Bernard_Widrow" title="Bernard Widrow">Bernard Widrow</a></li> <li><a href="/wiki/Joseph_Weizenbaum" title="Joseph Weizenbaum">Joseph Weizenbaum</a></li> <li><a href="/wiki/Seymour_Papert" title="Seymour Papert">Seymour Papert</a></li> <li><a href="/wiki/Seppo_Linnainmaa" title="Seppo Linnainmaa">Seppo Linnainmaa</a></li> <li><a href="/wiki/Paul_Werbos" title="Paul Werbos">Paul Werbos</a></li> <li><a href="/wiki/J%C3%BCrgen_Schmidhuber" title="Jürgen Schmidhuber">Jürgen Schmidhuber</a></li> <li><a href="/wiki/Yann_LeCun" title="Yann LeCun">Yann LeCun</a></li> <li><a href="/wiki/Geoffrey_Hinton" title="Geoffrey Hinton">Geoffrey Hinton</a></li> <li><a href="/wiki/John_Hopfield" title="John Hopfield">John Hopfield</a></li> <li><a href="/wiki/Yoshua_Bengio" title="Yoshua Bengio">Yoshua Bengio</a></li> <li><a href="/wiki/Lotfi_A._Zadeh" title="Lotfi A. Zadeh">Lotfi A. Zadeh</a></li> <li><a href="/wiki/Stephen_Grossberg" title="Stephen Grossberg">Stephen Grossberg</a></li> <li><a href="/wiki/Alex_Graves_(computer_scientist)" title="Alex Graves (computer scientist)">Alex Graves</a></li> <li><a href="/wiki/Andrew_Ng" title="Andrew Ng">Andrew Ng</a></li> <li><a href="/wiki/Fei-Fei_Li" title="Fei-Fei Li">Fei-Fei Li</a></li> <li><a href="/wiki/Alex_Krizhevsky" title="Alex Krizhevsky">Alex Krizhevsky</a></li> <li><a href="/wiki/Ilya_Sutskever" title="Ilya Sutskever">Ilya Sutskever</a></li> <li><a href="/wiki/Demis_Hassabis" title="Demis Hassabis">Demis Hassabis</a></li> <li><a href="/wiki/David_Silver_(computer_scientist)" title="David Silver (computer scientist)">David Silver</a></li> <li><a href="/wiki/Ian_Goodfellow" title="Ian Goodfellow">Ian Goodfellow</a></li> <li><a href="/wiki/Andrej_Karpathy" title="Andrej Karpathy">Andrej Karpathy</a></li></ul> </div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Architectures</th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/Neural_Turing_machine" title="Neural Turing machine">Neural Turing machine</a></li> <li><a href="/wiki/Differentiable_neural_computer" title="Differentiable neural computer">Differentiable neural computer</a></li> <li><a href="/wiki/Transformer_(deep_learning_architecture)" title="Transformer (deep learning architecture)">Transformer</a> <ul><li><a href="/wiki/Vision_transformer" title="Vision transformer">Vision transformer (ViT)</a></li></ul></li> <li><a href="/wiki/Recurrent_neural_network" title="Recurrent neural network">Recurrent neural network (RNN)</a></li> <li><a href="/wiki/Long_short-term_memory" title="Long short-term memory">Long short-term memory (LSTM)</a></li> <li><a href="/wiki/Gated_recurrent_unit" title="Gated recurrent unit">Gated recurrent unit (GRU)</a></li> <li><a href="/wiki/Echo_state_network" title="Echo state network">Echo state network</a></li> <li><a href="/wiki/Multilayer_perceptron" title="Multilayer perceptron">Multilayer perceptron (MLP)</a></li> <li><a href="/wiki/Convolutional_neural_network" title="Convolutional neural network">Convolutional neural network (CNN)</a></li> <li><a href="/wiki/Residual_neural_network" title="Residual neural network">Residual neural network (RNN)</a></li> <li><a href="/wiki/Highway_network" title="Highway network">Highway network</a></li> <li><a href="/wiki/Mamba_(deep_learning_architecture)" title="Mamba (deep learning architecture)">Mamba</a></li> <li><a href="/wiki/Autoencoder" title="Autoencoder">Autoencoder</a></li> <li><a href="/wiki/Variational_autoencoder" title="Variational autoencoder">Variational autoencoder (VAE)</a></li> <li><a href="/wiki/Generative_adversarial_network" title="Generative adversarial network">Generative adversarial network (GAN)</a></li> <li><a href="/wiki/Graph_neural_network" title="Graph neural network">Graph neural network (GNN)</a></li></ul> </div></td></tr><tr><td class="navbox-abovebelow" colspan="2"><div> <ul><li><a href="/wiki/File:Symbol_portal_class.svg" class="mw-file-description" title="Portal"><img alt="" src="//upload.wikimedia.org/wikipedia/en/thumb/e/e2/Symbol_portal_class.svg/16px-Symbol_portal_class.svg.png" decoding="async" width="16" height="16" class="mw-file-element" srcset="//upload.wikimedia.org/wikipedia/en/thumb/e/e2/Symbol_portal_class.svg/23px-Symbol_portal_class.svg.png 1.5x, //upload.wikimedia.org/wikipedia/en/thumb/e/e2/Symbol_portal_class.svg/31px-Symbol_portal_class.svg.png 2x" data-file-width="180" data-file-height="185" /></a> Portals <ul><li><a href="/wiki/Portal:Technology" title="Portal:Technology">Technology</a></li></ul></li> <li><img alt="" src="//upload.wikimedia.org/wikipedia/en/thumb/9/96/Symbol_category_class.svg/16px-Symbol_category_class.svg.png" decoding="async" width="16" height="16" class="mw-file-element" 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