{"id":144941,"date":"2015-04-28T00:14:23","date_gmt":"2015-04-28T00:14:23","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/group\/machine-teaching-group\/"},"modified":"2022-08-16T12:54:18","modified_gmt":"2022-08-16T19:54:18","slug":"machine-teaching-group","status":"publish","type":"msr-group","link":"https:\/\/www.microsoft.com\/en-us\/research\/group\/machine-teaching-group\/","title":{"rendered":"Machine Teaching Group"},"content":{"rendered":"<section class=\"mb-3 moray-highlight\">\n\t<div class=\"card-img-overlay mx-lg-0\">\n\t\t<div class=\"card-background  has-background- card-background--full-bleed\">\n\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"1920\" height=\"720\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2015\/04\/MachineTeachingHeader_FINAL.jpg\" class=\"attachment-full size-full\" alt=\"Machine Teaching Group header image\" style=\"object-position: 59% 52%\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2015\/04\/MachineTeachingHeader_FINAL.jpg 1920w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2015\/04\/MachineTeachingHeader_FINAL-300x113.jpg 300w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2015\/04\/MachineTeachingHeader_FINAL-768x288.jpg 768w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2015\/04\/MachineTeachingHeader_FINAL-1024x384.jpg 1024w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2015\/04\/MachineTeachingHeader_FINAL-1600x600.jpg 1600w\" sizes=\"(max-width: 1920px) 100vw, 1920px\" \/>\t\t<\/div>\n\t\t<!-- Foreground -->\n\t\t<div class=\"card-foreground d-flex mt-md-n5 my-lg-5 px-g px-lg-0\">\n\t\t\t<!-- Container -->\n\t\t\t<div class=\"container d-flex mt-md-n5 my-lg-5 align-self-center\">\n\t\t\t\t<!-- Card wrapper -->\n\t\t\t\t<div class=\"w-100 w-lg-col-5\">\n\t\t\t\t\t<!-- Card -->\n\t\t\t\t\t<div class=\"card material-md-card py-5 px-md-5\">\n\t\t\t\t\t\t<div class=\"card-body \">\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\n<h1 id=\"machine-teaching-group\" class=\"h2\">Machine Teaching Group<\/h1>\n\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/div>\n\t<\/div>\n<\/section>\n\n\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-1 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p>Machine teaching seeks to gain knowledge from people rather than extracting knowledge from data alone. Any information processing skill that is teachable to a human, should be easily teachable to a machine.<\/p>\n\n\n\n<p>Think about a business process that involves getting information out of unformatted text, such as email or other documents, such as determining how many of a certain product had been quoted to each customer last year? That might involve combing through documents, knowing that particular documents are quote documents and what part of the text includes the part being quoted and includes the customer name. Those are not difficult tasks for a human to learn, but today they can be difficult to automate because they\u2019re customized to a specific business\u2019s industry and document formats.<\/p>\n\n\n\n<p>Our research is focused on teaching machines to do these types of specialized tasks in a way that is easy, fast, and accessible to people without machine learning expertise.<\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" title=\"Machine Teaching Overview\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube-nocookie.com\/embed\/3nlxPMpr_JQ?feature=oembed&rel=0\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div><\/figure>\n<\/div>\n<\/div>\n\n\n\n<p>Traditional machine learning requires volumes of labeled data that can be time consuming and expensive to produce. Machine teaching leverages the human capability to decompose and explain concepts to train machine leaning models, which is much more efficient than using labels alone. With the human teacher and the machine learning model working together in a real-time interactive process, we can dramatically speed up model-building time.<\/p>\n\n\n\n<div class=\"wp-block-media-text has-vertical-margin-small  has-vertical-padding-none  is-stacked-on-mobile is-style-gray-background\" style=\"grid-template-columns:60% auto\" data-bi-an=\"media-text\"><figure class=\"wp-block-media-text__media\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"721\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2015\/04\/machine-teaching-process-1024x721.jpg\" alt=\"Machine teaching process graphic\" class=\"wp-image-577848 size-full\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2015\/04\/machine-teaching-process-1024x721.jpg 1024w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2015\/04\/machine-teaching-process-300x211.jpg 300w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2015\/04\/machine-teaching-process-768x540.jpg 768w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2015\/04\/machine-teaching-process.jpg 1478w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure><div class=\"wp-block-media-text__content\" data-bi-an=\"media-text\">\n<h2 id=\"machine-teaching-process\">Machine teaching process<\/h2>\n\n\n\n<p>We work on making the process of building, debugging, and using ML models more efficient. We design our tools for the end-to-end development cycle, knowing that custom models must be deployed and maintained to serve their ultimate purpose. Our projects and products, such as <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/project\/platform-for-interactive-concept-learning-picl\/\">PICL<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> and <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/www.luis.ai\/home\" target=\"_blank\" rel=\"noreferrer noopener\">LUIS<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>, are designed to be frictionless and enable people without data science knowledge to leverage the power of machine learning through machine teaching.<\/p>\n\n\n\n<p><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/www.luis.ai\/home\" target=\"_blank\" rel=\"noreferrer noopener\">Try Machine Teaching with Language Understanding (LUIS) ><span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/p>\n<\/div><\/div>\n\n\n\n<h2 id=\"benefits-of-machine-teaching\" class=\"has-text-align-center\">Benefits of machine teaching<\/h2>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-2 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p><strong>Data science expertise not required<\/strong><\/p>\n\n\n\n<p>Machine teaching abstracts the information used to teach the machine away from the algorithm used to train the model. By separating the teaching information from the algorithm, we can allow the algorithms and the teaching language to innovate independently and the teacher doesn\u2019t need to understand machine learning algorithms.<\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p><strong>Build custom models fast<\/strong><\/p>\n\n\n\n<p>Because machine teaching leverages information from the teacher in addition to labels, we can build models with fewer labels than traditional methods. Labeling examples is part of the data exploration and debugging process and not a rote task; it\u2019s quick and easy to start with no labeled data. A single subject matter expert can quickly build an effective model without armies of labelers.<\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p><strong>Easily updateable model<\/strong><\/p>\n\n\n\n<p>Decomposition enables easier debugging. Our tools are built with the end-to-end development cycle in mind. When failures are found in production, they can be quickly added to the labeled data and new features can be added to address changes in the concept.<\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p><strong>Share and reuse<\/strong><\/p>\n\n\n\n<p>Decomposition allows us to leverage work done by others and build on it. \u200b\u200bIf someone has built models to recognize part numbers and prices, they could all be used as inputs to a model that is used to extract bills of materials. Those same models might also be useful in classifying documents as sales receipts. We can reuse and customize existing models to fit specific problems.<\/p>\n<\/div>\n<\/div>\n\n\n","protected":false},"excerpt":{"rendered":"<p>Machine teaching leverages the human capability to decompose and explain concepts to train machine leaning models, which is much more efficient than using labels alone.<\/p>\n","protected":false},"featured_media":580645,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"msr_group_start":"","footnotes":""},"research-area":[13556,13554,13555],"msr-group-type":[243694],"msr-locale":[268875],"msr-impact-theme":[],"class_list":["post-144941","msr-group","type-msr-group","status-publish","has-post-thumbnail","hentry","msr-research-area-artificial-intelligence","msr-research-area-human-computer-interaction","msr-research-area-search-information-retrieval","msr-group-type-group","msr-locale-en_us"],"msr_group_start":"","msr_detailed_description":"","msr_further_details":"","msr_hero_images":[],"msr_research_lab":[],"related-researchers":[{"type":"user_nicename","display_name":"Geoff Cox","user_id":37258,"people_section":"Machine Teaching Innovation\u00a0Team\u00a0in Office","alias":"gcox"},{"type":"user_nicename","display_name":"Alicia Edelman Pelton","user_id":30945,"people_section":"Machine Teaching Innovation\u00a0Team\u00a0in Office","alias":"aliciaep"},{"type":"user_nicename","display_name":"Saleema Amershi","user_id":33505,"people_section":"Microsoft Research Collaborators","alias":"samershi"},{"type":"user_nicename","display_name":"Gonzalo Ramos","user_id":36419,"people_section":"Microsoft Research Collaborators","alias":"goramos"},{"type":"user_nicename","display_name":"Jina Suh","user_id":32311,"people_section":"Microsoft Research Collaborators","alias":"jinsuh"},{"type":"guest","display_name":"Leon Bottou","user_id":395804,"people_section":"Alumni","alias":""},{"type":"guest","display_name":"Denis Charles","user_id":395798,"people_section":"Alumni","alias":""},{"type":"guest","display_name":"David Grangier","user_id":395801,"people_section":"Alumni","alias":""},{"type":"guest","display_name":"Aparna Lakshmiratan","user_id":395795,"people_section":"Alumni","alias":""},{"type":"guest","display_name":"Jerry Zhu","user_id":399809,"people_section":"Alumni","alias":""},{"type":"guest","display_name":"Carlos Garcia Jurado Suarez","user_id":399812,"people_section":"Alumni","alias":""}],"related-publications":[478359,478377,400622,375914,323909,323960,298592,452211,238286,298601,168604,168046,167737,167870,167516,166489,166751,163295,576789,607572,634857],"related-downloads":[],"related-videos":[],"related-projects":[],"related-events":[],"related-opportunities":[],"related-posts":[],"tab-content":[],"msr_impact_theme":[],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-group\/144941"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-group"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-group"}],"version-history":[{"count":25,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-group\/144941\/revisions"}],"predecessor-version":[{"id":870153,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-group\/144941\/revisions\/870153"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/580645"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=144941"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=144941"},{"taxonomy":"msr-group-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-group-type?post=144941"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=144941"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=144941"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}