{"id":1172359,"date":"2026-05-27T09:00:00","date_gmt":"2026-05-27T16:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/blog\/extending-human-intelligence-through-ai\/"},"modified":"2026-05-27T09:41:26","modified_gmt":"2026-05-27T16:41:26","slug":"extending-human-intelligence-through-ai","status":"publish","type":"post","link":"https:\/\/www.microsoft.com\/en-us\/research\/blog\/extending-human-intelligence-through-ai\/","title":{"rendered":"Extending Human Intelligence Through AI"},"content":{"rendered":"\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"2560\" height=\"1441\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/ExtendingHIthroughAI-BlogHeroFeature-1400x788-1-scaled.jpg\" alt=\"Three icons (speech bubble, handshake, and interconnected circles) on a blue and green gradient background.  \" class=\"wp-image-1172711\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/ExtendingHIthroughAI-BlogHeroFeature-1400x788-1-scaled.jpg 2560w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/ExtendingHIthroughAI-BlogHeroFeature-1400x788-1-300x169.jpg 300w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/ExtendingHIthroughAI-BlogHeroFeature-1400x788-1-1024x576.jpg 1024w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/ExtendingHIthroughAI-BlogHeroFeature-1400x788-1-768x432.jpg 768w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/ExtendingHIthroughAI-BlogHeroFeature-1400x788-1-1536x865.jpg 1536w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/ExtendingHIthroughAI-BlogHeroFeature-1400x788-1-2048x1153.jpg 2048w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/ExtendingHIthroughAI-BlogHeroFeature-1400x788-1-1066x600.jpg 1066w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/ExtendingHIthroughAI-BlogHeroFeature-1400x788-1-655x368.jpg 655w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/ExtendingHIthroughAI-BlogHeroFeature-1400x788-1-240x135.jpg 240w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/ExtendingHIthroughAI-BlogHeroFeature-1400x788-1-640x360.jpg 640w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/ExtendingHIthroughAI-BlogHeroFeature-1400x788-1-960x540.jpg 960w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/ExtendingHIthroughAI-BlogHeroFeature-1400x788-1-1280x720.jpg 1280w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/ExtendingHIthroughAI-BlogHeroFeature-1400x788-1-1920x1080.jpg 1920w\" sizes=\"auto, (max-width: 2560px) 100vw, 2560px\" \/><\/figure>\n\n\n\n<div style=\"padding-bottom:0; padding-top:0\" class=\"wp-block-msr-immersive-section alignfull row wp-block-msr-immersive-section\">\n\t\n\t<div class=\"container\">\n\t\t<div class=\"wp-block-msr-immersive-section__inner wp-block-msr-immersive-section__inner--narrow\">\n\t\t\t<div class=\"wp-block-columns mb-10 pb-1 pr-1 is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\" style=\"box-shadow:var(--wp--preset--shadow--outlined)\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<h2 class=\"wp-block-heading h3\" id=\"at-a-glance\">At a glance<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Modern AI systems are powerful not because they replicate human intelligence, but because they&nbsp;presuppose it, by&nbsp;extending&nbsp;structures already present in human cognition and language.<\/li>\n\n\n\n<li>This perspective helps explain both AI\u2019s remarkable capabilities and its recurring boundaries, including hallucinations and breakdowns in reasoning.<\/li>\n\n\n\n<li>This research argues that AI safety is a system-level challenge, shifting attention from \u201crogue AI\u201d narratives toward harnessing engineering and governance.<\/li>\n\n\n\n<li>Understanding AI as an extension of human intelligence\u2014not a replacement for it\u2014offers a more grounded path for building trustworthy AI systems.<\/li>\n<\/ul>\n<\/div>\n<\/div>\t\t<\/div>\n\t<\/div>\n\n\t<\/div>\n\n\n\n<p>AI&nbsp;systems today can write essays, generate code, summarize complex ideas, and carry on conversations with remarkable fluency. Yet those same systems still struggle with tasks humans find intuitive: reliably tracking objects through change, reasoning compositionally in unfamiliar situations, or distinguishing truth from plausible fiction. These contradictions have fueled polarized debates about AI.&nbsp;Some see current systems as early forms of human-like intelligence; others dismiss them as sophisticated autocomplete.&nbsp;<\/p>\n\n\n\n<p>In recent interdisciplinary work &#8211; including\u00a0Adam Frank, Marcelo Gleiser, and Evan Thompson&#8217;s\u00a0<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/www.penguinrandomhouse.com\/books\/739505\/the-blind-spot-by-adam-frank-marcelo-gleiser-and-evan-thompson\/\" target=\"_blank\" rel=\"noopener noreferrer\"><em>The Blind Spot<\/em><span class=\"sr-only\"> (opens in new tab)<\/span><\/a>\u00a0and DeepMind researcher Alexander Lerchner&#8217;s\u00a0<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/deepmind.google\/research\/publications\/231971\/\" target=\"_blank\" rel=\"noopener noreferrer\"><em>The Abstraction Fallacy<\/em><span class=\"sr-only\"> (opens in new tab)<\/span><\/a>\u00a0&#8211; a different picture is emerging. Rather than asking whether AI systems are becoming intelligent in the human sense, these approaches ask a more basic question:\u00a0What\u00a0if AI systems\u00a0work\u00a0<em>because<\/em>\u00a0they rely on structures that are rooted in human cognition? This shift in perspective, which draws on the phenomenology of Edmund Husserl, helps make sense of both the capabilities and the limits of modern AI.\u00a0<\/p>\n\n\n\n<p>In our recent paper,&nbsp;<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/the-origins-of-artificial-intelligence-in-natural-intelligence\/\" target=\"_blank\" rel=\"noreferrer noopener\">The Origins of Artificial Intelligence in Natural Intelligence<\/a>, we argue that modern AI systems are best understood&nbsp;neither as human minds nor as trivial statistical tricks. Instead, they extend structures that originate in human cognition itself.&nbsp;Further&nbsp;drawing on the phenomenology&nbsp;of Husserl, the paper proposes that language already&nbsp;contains&nbsp;sedimented structures of human understanding&nbsp;\u2014structures that AI systems learn to model and extend. This perspective helps explain both the capabilities and the boundaries of contemporary AI.&nbsp;&nbsp;<\/p>\n\n\n\n<p>Human perception is not simply passive reception of sensory data. We experience the world as stable things unfolding through change: a cup&nbsp;remains&nbsp;the same cup as we move around it; a melody&nbsp;remains&nbsp;recognizable even as individual notes pass away. Language&nbsp;emerges&nbsp;by expressing these stable structures in conceptual form. Words like \u201cred,\u201d \u201cround,\u201d or \u201clarger than\u201d articulate relationships that originate&nbsp;in&nbsp;lived&nbsp;experience.&nbsp;<\/p>\n\n\n\n<p>Large language models learn statistical relationships within this linguistic world. They capture how concepts tend to relate across enormous bodies of human writing. This explains why AI systems can produce coherent responses across many domains. But it also explains why they&nbsp;hallucinate. Humans&nbsp;remain&nbsp;answerable to the world: experience continually corrects our expectations and beliefs. AI systems, by contrast, extend patterns within text itself. They can continue a line of reasoning with remarkable fluency, but they lack the lived engagement with the world that anchors meaning and truth.<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1400\" height=\"906\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/ai_extends_human_cognition@4x_1400px.png\" alt=\"How AI extends human cognition | diagram\" class=\"wp-image-1172363\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/ai_extends_human_cognition@4x_1400px.png 1400w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/ai_extends_human_cognition@4x_1400px-300x194.png 300w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/ai_extends_human_cognition@4x_1400px-1024x663.png 1024w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/ai_extends_human_cognition@4x_1400px-768x497.png 768w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/ai_extends_human_cognition@4x_1400px-240x155.png 240w\" sizes=\"auto, (max-width: 1400px) 100vw, 1400px\" \/><figcaption class=\"wp-element-caption\">AI Extends Human Cognition&nbsp;<\/figcaption><\/figure>\n\n\n\n<p>This framework helps explain several recurring challenges in AI research. One is the \u201ccompositionality gap\u201d\u2014the tendency for language models to perform well on familiar reasoning patterns while failing when asked to combine concepts in genuinely novel ways. Research increasingly shows that larger models improve fluency and factual recall much faster than they improve true compositional reasoning. From our perspective, this is not simply an engineering limitation but a structural boundary: AI systems can extend patterns already sedimented in language, but they do not&nbsp;possess&nbsp;the world-directed understanding that allows humans to generate genuinely new conceptual relations.&nbsp;<\/p>\n\n\n\n<p>A similar pattern appears in multimodal systems that combine language and vision. These systems can often label images correctly while still failing at robust reasoning about objects and their parts. They learn correlations between visual patterns and language rather than perceiving stable objects unfolding through time in the way humans do. The result is systems that can appear impressively fluent while&nbsp;remaining surprisingly brittle outside familiar patterns.&nbsp;<\/p>\n\n\n\n<p>This perspective also&nbsp;reframes&nbsp;debates about AI safety. Public discussion often swings between fears of \u201crogue superintelligence\u201d and claims that AI poses little meaningful risk. Our research suggests that both extremes misunderstand the nature of current systems. The most immediate risks arise not because AI possesses human-like intentions, but because it can extend patterns of reasoning without reflective responsibility to the world. Systems can generate persuasive but ungrounded outputs, automate flawed decisions at scale, or execute harmful actions if embedded in poorly governed environments.<\/p>\n\n\n\n<p>This helps explain why AI safety is increasingly shifting from model safety to system safety. In practice, organizations already rely on layered safeguards\u2014what the industry increasingly calls \u201charnesses\u201d\u2014to constrain,&nbsp;validate, and&nbsp;monitor&nbsp;AI behavior. Rather than temporary patches, our paper argues that these mechanisms reflect something fundamental about AI architecture itself:&nbsp;trustworthy behavior&nbsp;emerges&nbsp;from the work of builders of AI systems responsible for their behavior, a responsibility that cannot be delegated to or shared with models.<\/p>\n\n\n\n<p>This interpretation aligns closely with how enterprises increasingly approach trustworthy AI deployment. Organizations need systems that can extend human intelligence while remaining governable, auditable, and aligned with human oversight. Understanding AI as a derived form of intelligence clarifies why layered governance, evaluation, and operational controls matter so deeply.<\/p>\n\n\n\n\t<div class=\"border-bottom border-top border-gray-300 mt-5 mb-5 msr-promo text-center text-md-left alignwide\" data-bi-aN=\"promo\" data-bi-id=\"670821\">\n\t\t\n\n\t\t<p class=\"msr-promo__label text-gray-800 text-center text-uppercase\">\n\t\t<span class=\"px-4 bg-white display-inline-block font-weight-semibold small\">Spotlight: Microsoft research newsletter<\/span>\n\t<\/p>\n\t\n\t<div class=\"row pt-3 pb-4 align-items-center\">\n\t\t\t\t\t\t<div class=\"msr-promo__media col-12 col-md-5\">\n\t\t\t\t<a class=\"bg-gray-300 display-block\" href=\"https:\/\/info.microsoft.com\/ww-landing-microsoft-research-newsletter.html\" aria-label=\"Microsoft Research Newsletter\" data-bi-cN=\"Microsoft Research Newsletter\" target=\"_blank\">\n\t\t\t\t\t<img decoding=\"async\" class=\"w-100 display-block\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2019\/09\/Newsletter_Banner_08_2019_v1_1920x1080.png\" alt=\"\" \/>\n\t\t\t\t<\/a>\n\t\t\t<\/div>\n\t\t\t\n\t\t\t<div class=\"msr-promo__content p-3 px-5 col-12 col-md\">\n\n\t\t\t\t\t\t\t\t\t<h2 class=\"h4\">Microsoft Research Newsletter<\/h2>\n\t\t\t\t\n\t\t\t\t\t\t\t\t<p id=\"microsoft-research-newsletter\" class=\"large\">Stay connected to the research community at Microsoft.<\/p>\n\t\t\t\t\n\t\t\t\t\t\t\t\t<div class=\"wp-block-buttons justify-content-center justify-content-md-start\">\n\t\t\t\t\t<div class=\"wp-block-button is-style-fill-chevron\">\n\t\t\t\t\t\t<a href=\"https:\/\/info.microsoft.com\/ww-landing-microsoft-research-newsletter.html\" aria-describedby=\"microsoft-research-newsletter\" class=\"btn btn-brand glyph-append glyph-append-chevron-right\" data-bi-cN=\"Microsoft Research Newsletter\" target=\"_blank\">\n\t\t\t\t\t\t\tSubscribe today\t\t\t\t\t\t<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div><!--\/.msr-promo__content-->\n\t<\/div><!--\/.msr-promo__inner-wrap-->\n\t<\/div><!--\/.msr-promo-->\n\t\n\n\n<p>Looking ahead, we believe phenomenology offers more than a critique of AI\u2014it offers a framework for understanding its promise. AI systems reveal something profound about human cognition itself: that meaning can be formalized, extended, and scaled in powerful new ways.&nbsp;<s>&nbsp;<\/s>The central societal risk of AI thus turns out to be kicking away the ladder of its origins in human experience and cognition&nbsp;&#8211;&nbsp;misinterpreting&nbsp;AI as a rival&nbsp;intelligence&nbsp;that diminishes our humanity&nbsp;and thus, in turn, diminishes the&nbsp;true&nbsp;promise of AI itself.&nbsp;<\/p>\n\n\n\n<p>The question, then, is not whether AI will replace human intelligence. It is how we can responsibly build systems that extend human understanding while remaining grounded in the world from which that understanding arises. If we mistake AI systems for autonomous minds, we risk over-trusting them. If we dismiss them as trivial tricks, we risk overlooking one of the most important technological developments of our time. A more grounded interpretation recognizes both truths at once: AI is a genuine extension of human intelligence\u2014and precisely because of that, humans remain responsible for how it is understood, governed, and used.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Understanding AI as an extension of human intelligence\u2014not a replacement for it\u2014offers a more grounded path for building trustworthy AI systems.<\/p>\n","protected":false},"author":43868,"featured_media":1172711,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_hide_image_in_river":0,"footnotes":""},"categories":[1],"tags":[],"research-area":[13556],"msr-region":[],"msr-event-type":[],"msr-locale":[268875],"msr-post-option":[243984],"msr-impact-theme":[],"msr-promo-type":[],"msr-podcast-series":[],"class_list":["post-1172359","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-research-blog","msr-research-area-artificial-intelligence","msr-locale-en_us","msr-post-option-blog-homepage-featured"],"msr_event_details":{"start":"","end":"","location":""},"podcast_url":"","podcast_episode":"","msr_research_lab":[],"msr_impact_theme":[],"related-publications":[],"related-downloads":[],"related-videos":[],"related-academic-programs":[],"related-groups":[],"related-projects":[],"related-events":[],"related-researchers":[{"type":"user_nicename","value":"Ken Archer","user_id":44157,"display_name":"Ken Archer","author_link":"<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/kenarcher\/\" aria-label=\"Visit the profile page for Ken Archer\">Ken Archer<\/a>","is_active":false,"last_first":"Archer, Ken","people_section":0,"alias":"kenarcher"},{"type":"guest","value":"harald-wiltsche","user_id":"1172360","display_name":"Harald Wiltsche","author_link":"<a href=\"https:\/\/liu.se\/en\/employee\/harwi67\" aria-label=\"Visit the profile page for Harald Wiltsche\">Harald Wiltsche<\/a>","is_active":true,"last_first":"Wiltsche, Harald","people_section":0,"alias":"harald-wiltsche"}],"msr_type":"Post","featured_image_thumbnail":"<img width=\"960\" height=\"540\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/ExtendingHIthroughAI-BlogHeroFeature-1400x788-1-960x540.jpg\" class=\"img-object-cover\" alt=\"Three icons (speech bubble, handshake, and interconnected circles) on a blue and green gradient background.\" decoding=\"async\" loading=\"lazy\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/ExtendingHIthroughAI-BlogHeroFeature-1400x788-1-960x540.jpg 960w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/ExtendingHIthroughAI-BlogHeroFeature-1400x788-1-300x169.jpg 300w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/ExtendingHIthroughAI-BlogHeroFeature-1400x788-1-1024x576.jpg 1024w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/ExtendingHIthroughAI-BlogHeroFeature-1400x788-1-768x432.jpg 768w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/ExtendingHIthroughAI-BlogHeroFeature-1400x788-1-1536x865.jpg 1536w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/ExtendingHIthroughAI-BlogHeroFeature-1400x788-1-2048x1153.jpg 2048w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/ExtendingHIthroughAI-BlogHeroFeature-1400x788-1-1066x600.jpg 1066w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/ExtendingHIthroughAI-BlogHeroFeature-1400x788-1-655x368.jpg 655w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/ExtendingHIthroughAI-BlogHeroFeature-1400x788-1-240x135.jpg 240w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/ExtendingHIthroughAI-BlogHeroFeature-1400x788-1-640x360.jpg 640w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/ExtendingHIthroughAI-BlogHeroFeature-1400x788-1-1280x720.jpg 1280w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/ExtendingHIthroughAI-BlogHeroFeature-1400x788-1-1920x1080.jpg 1920w\" sizes=\"auto, (max-width: 960px) 100vw, 960px\" \/>","byline":"<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/kenarcher\/\" title=\"Go to researcher profile for Ken Archer\" aria-label=\"Go to researcher profile for Ken Archer\" data-bi-type=\"byline author\" data-bi-cN=\"Ken Archer\">Ken Archer<\/a> and <a href=\"https:\/\/liu.se\/en\/employee\/harwi67\" title=\"Go to researcher profile for Harald Wiltsche\" aria-label=\"Go to researcher profile for Harald Wiltsche\" data-bi-type=\"byline author\" data-bi-cN=\"Harald Wiltsche\">Harald Wiltsche<\/a>","formattedDate":"May 27, 2026","formattedExcerpt":"Understanding AI as an extension of human intelligence\u2014not a replacement for it\u2014offers a more grounded path for building trustworthy AI systems.","locale":{"slug":"en_us","name":"English","native":"","english":"English"},"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/posts\/1172359","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/users\/43868"}],"replies":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/comments?post=1172359"}],"version-history":[{"count":13,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/posts\/1172359\/revisions"}],"predecessor-version":[{"id":1173592,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/posts\/1172359\/revisions\/1173592"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/1172711"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=1172359"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/categories?post=1172359"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/tags?post=1172359"},{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=1172359"},{"taxonomy":"msr-region","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-region?post=1172359"},{"taxonomy":"msr-event-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event-type?post=1172359"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=1172359"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=1172359"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=1172359"},{"taxonomy":"msr-promo-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-promo-type?post=1172359"},{"taxonomy":"msr-podcast-series","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-podcast-series?post=1172359"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}