{"id":1004556,"date":"2024-02-07T09:00:00","date_gmt":"2024-02-07T17:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?p=1004556"},"modified":"2024-02-22T11:18:43","modified_gmt":"2024-02-22T19:18:43","slug":"research-focus-week-of-february-5-2024","status":"publish","type":"post","link":"https:\/\/www.microsoft.com\/en-us\/research\/blog\/research-focus-week-of-february-5-2024\/","title":{"rendered":"Research Focus: Week of February 5, 2024"},"content":{"rendered":"\n<figure class=\"wp-block-pullquote\"><blockquote><p><em class=\"\">Welcome to Research Focus, a series of blog posts that highlights notable publications, events, code\/datasets, new hires and other milestones from across the research community at Microsoft.<\/em><\/p><\/blockquote><\/figure>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1400\" height=\"788\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/02\/RF34-BlogHeroFeature-1400x788-1.png\" alt=\"Research Focus Week of February 5, 2024\" class=\"wp-image-1004565\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/02\/RF34-BlogHeroFeature-1400x788-1.png 1400w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/02\/RF34-BlogHeroFeature-1400x788-1-300x169.png 300w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/02\/RF34-BlogHeroFeature-1400x788-1-1024x576.png 1024w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/02\/RF34-BlogHeroFeature-1400x788-1-768x432.png 768w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/02\/RF34-BlogHeroFeature-1400x788-1-1066x600.png 1066w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/02\/RF34-BlogHeroFeature-1400x788-1-655x368.png 655w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/02\/RF34-BlogHeroFeature-1400x788-1-343x193.png 343w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/02\/RF34-BlogHeroFeature-1400x788-1-240x135.png 240w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/02\/RF34-BlogHeroFeature-1400x788-1-640x360.png 640w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/02\/RF34-BlogHeroFeature-1400x788-1-960x540.png 960w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/02\/RF34-BlogHeroFeature-1400x788-1-1280x720.png 1280w\" sizes=\"(max-width: 1400px) 100vw, 1400px\" \/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading h6 has-blue-color has-text-color has-link-color wp-elements-fbe88093d062c629283ec625f92315a8\" id=\"event-recap\">EVENT RECAP<\/h3>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"microsoft-research-forum-series-kicks-off-with-focus-on-the-promise-and-challenges-of-ai\">Microsoft Research Forum series kicks off with focus on the promise and challenges of AI<\/h2>\n\n\n\n<p>With a look back at the incredible changes of 2023 and a look ahead at the tangible advances to come, the inaugural Microsoft Research Forum explored bold new ideas and important issues in the era of general AI. Leaders from Microsoft Research, including the <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/lab\/ai-frontiers\/\">AI Frontiers<\/a> team and the <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/lab\/microsoft-research-ai4science\/\">AI4Science lab<\/a>, discussed the importance of openness and collaboration to enable successful and responsible AI research.<\/p>\n\n\n\n<p><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/petelee\/\">Peter Lee<\/a>, CVP, Microsoft Research and Incubations, led off the discussion, followed by a panel exploring some of the biggest potential AI breakthroughs, along with challenges to overcome. This includes:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Building AI systems that become helpers in the physical world&nbsp;<\/li>\n\n\n\n<li>Uncovering the building blocks of human reasoning&nbsp;<\/li>\n\n\n\n<li>Making AI technology smaller and less costly, to improve performance and availability&nbsp;&nbsp;<\/li>\n\n\n\n<li>Helping AI learn from people that use it, rather than simply answering questions&nbsp;<\/li>\n<\/ul>\n\n\n\n<p>In the \u201clightning round,\u201d Microsoft researchers explored current work to improve pretrained large language models, understand and evaluate foundation models, facilitate breakthroughs in molecular science, augment human decision making, and improve visual storytelling.<\/p>\n\n\n\n<p>To learn more, check out <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/blog\/microsoft-research-forum-new-series-explores-bold-ideas-in-technology-research-in-the-era-of-ai\/\">this recap<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> and browse the <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/quarterly-brief\/jan-2024-brief\/?OCID=msr_researchforum_MCR_Blog_Promo\">on-demand session replays<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>. Be sure to <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/researchforum.microsoft.com\/?OCID=msr_researchforum_MSR_Research_Focus\" target=\"_blank\" rel=\"noreferrer noopener\">register for upcoming episodes<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>.<\/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=\"1085520\">\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\">Microsoft research podcast<\/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\" href=\"https:\/\/www.microsoft.com\/en-us\/research\/podcast\/collaborators-silica-in-space-with-richard-black-and-dexter-greene\/\" aria-label=\"Collaborators: Silica in space with Richard Black and Dexter Greene\" data-bi-cN=\"Collaborators: Silica in space with Richard Black and Dexter Greene\" target=\"_blank\">\n\t\t\t\t\t<img decoding=\"async\" class=\"w-100 display-block\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/09\/Richard-and-Dexter_Collaborators_Hero_Feature_No_Text_1400x788-1.jpg\" alt=\"Headshots of Richard Black and Dexter Greene for the Microsoft Research Podcast\" \/>\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\">Collaborators: Silica in space with Richard Black and Dexter Greene<\/h2>\n\t\t\t\t\n\t\t\t\t\t\t\t\t<p class=\"large\">College freshman Dexter Greene and Microsoft research manager Richard Black discuss how technology that stores data in glass is supporting students as they expand earlier efforts to communicate what it means to be human to extraterrestrials.<\/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\">\n\t\t\t\t\t\t<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/podcast\/collaborators-silica-in-space-with-richard-black-and-dexter-greene\/\" class=\"btn btn-brand glyph-append glyph-append-chevron-right\" aria-label=\"Listen now\" data-bi-cN=\"Collaborators: Silica in space with Richard Black and Dexter Greene\" target=\"_blank\">\n\t\t\t\t\t\t\tListen now\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<h3 class=\"wp-block-heading h6 has-blue-color has-text-color has-link-color wp-elements-a584a2137da4151ecbde93fba771f798\" id=\"new-research\">NEW RESEARCH<\/h3>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"the-truth-is-in-there-improving-reasoning-in-language-models-with-layer-selective-rank-reduction\">The Truth is in There: Improving Reasoning in Language Models with Layer-Selective Rank Reduction<\/h2>\n\n\n\n<p>Transformer-based large language models (LLMs) have become a fixture in machine learning. Correspondingly, significant resources are allocated towards research to further advance this technology, typically resulting in models of increasing size that are trained on increasing amounts of data.<\/p>\n\n\n\n<p>In a recent paper, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/the-truth-is-in-there-improving-reasoning-in-language-models-with-layer-selective-rank-reduction\/\">The Truth is in There: Improving Reasoning in Language Models with Layer-Selective Rank Reduction<\/a>, researchers from Microsoft demonstrate a surprising result: that it is possible to significantly improve LLM performance by selectively removing higher-order components of their constituent weight matrices. As covered in a <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/quarterly-brief\/jan-2024-brief\/articles\/improving-reasoning-in-language-models-with-laser-layer-selective-rank-reduction\/\">Microsoft Research Forum lightning talk<\/a>, this simple intervention\u2014LAyer-SElective Rank reduction (LASER)\u2014can be done on a model after training has been completed, and requires no additional parameters or data. In extensive experiments, the researchers demonstrate the generality of this finding across language models and datasets. They provide in-depth analyses offering insights into both when LASER is effective and the mechanism by which it operates.<\/p>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-center is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-1 wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button is-style-outline is-style-outline--c8200123302c810f083202889d885eb8\"><a data-bi-type=\"button\" class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/the-truth-is-in-there-improving-reasoning-in-language-models-with-layer-selective-rank-reduction\/\">Read the paper<\/a><\/div>\n<\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity is-style-dots\"\/>\n\n\n\n<h3 class=\"wp-block-heading h6 has-blue-color has-text-color has-link-color wp-elements-21d8108ee594aad478409a8aa618b2ee\" id=\"new-research-1\">NEW RESEARCH<\/h3>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"cache-efficient-top-k-aggregation-over-high-cardinality-large-datasets\">Cache-Efficient Top-k Aggregation over High Cardinality Large Datasets<\/h2>\n\n\n\n<p>Business intelligence tools make it easy to analyze large amounts of data. In these tools, top-k aggregation queries are used to summarize and identify important groups of data. These queries are usually processed by computing exact aggregates for all groups and then selecting the groups with the top-k aggregate values. However, this can be inefficient for high-cardinality large datasets, where intermediate results may not fit within the local cache of multi-core processors, leading to excessive data movement.<\/p>\n\n\n\n<p>Researchers from Microsoft, in their recent paper: <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/cache-efficient-top-k-aggregation-over-high-cardinality-large-datasets\/\">Cache-Efficient Top-k Aggregation over High Cardinality Large Datasets<\/a>, introduce a new cache-conscious algorithm to address this. The algorithm efficiently computes exact top-k aggregates without fully processing all groups. Aggregation over large datasets requires multiple passes of data partitioning and repartitioning, thereby presenting a significant opportunity to reduce partitioning overhead for top-k aggregation queries. The algorithm leverages skewness in data distribution to select a small set of candidate groups for early aggregation. This helps eliminate many non-candidate<s>s<\/s> group partitions through efficient partitioning techniques and coarse-grained statistics without computing exact aggregation. Empirical evaluation using both real-world and synthetic datasets demonstrate that the algorithm achieves a median speed-up of over 3x for monotonic aggregation functions and 1.4x for non-monotonic functions, compared to existing cache-conscious aggregation methods, across standard k value ranges (1 to 100).<\/p>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-center is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-2 wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button is-style-outline is-style-outline--68bc11d4495ba9a8d73bf6b83d320256\"><a data-bi-type=\"button\" class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/cache-efficient-top-k-aggregation-over-high-cardinality-large-datasets\/\">Read the paper<\/a><\/div>\n<\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity is-style-dots\"\/>\n\n\n\n<h3 class=\"wp-block-heading h6 has-blue-color has-text-color has-link-color wp-elements-d0c45191bd84884375870a119b31f3d8\" id=\"awards\">AWARDS<\/h3>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"six-microsoft-researchers-named-2023-acm-fellows\">Six Microsoft researchers named 2023 ACM Fellows<\/h2>\n\n\n\n<p>The Association for Computing Machinery\u2019s (ACM) annual fellows award recognizes people who make transformative contributions to computing science and technology. For 2023, the global organization named six researchers from Microsoft among its 68 award recipients.<\/p>\n\n\n\n<p><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/jfgao\/\">Jianfeng Gao<\/a> \u2013 VP and Distinguished Scientist<br><em>For contributions to machine learning for web search, natural language processing, and conversational systems<\/em><\/p>\n\n\n\n<p><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/sumitg\/\">Sumit Gulwani<\/a> \u2013 Partner Research Manager<br><em>For contributions to AI-assisted programming for developers, data scientists, end users, and students<\/em><\/p>\n\n\n\n<p><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/nicimm\/\">Nicole Immorlica<\/a> \u2013 Senior Principal Researcher<br><em>For contributions to economics and computation including market design, auctions, and social networks<\/em><\/p>\n\n\n\n<p><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/ssaroiu\/\">Stefan Saroiu<\/a> \u2013 Senior Principal Researcher<br><em>For contributions to memory security and trusted computing<\/em><\/p>\n\n\n\n<p><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/manik\/\">Manik Varma<\/a> \u2013 VP and Distinguished Scientist<br><em>For contributions to machine learning and its applications<\/em><\/p>\n\n\n\n<p><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/xingx\/\">Xing Xie<\/a> &#8211; Senior Principal Research Manager<br><em>For contributions to spatial data mining and recommendation systems<\/em><\/p>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-center is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-3 wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a data-bi-type=\"button\" class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/www.acm.org\/media-center\/2024\/january\/fellows-2023\" target=\"_blank\" rel=\"noreferrer noopener\">2023 ACM Fellows<\/a><\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Research Focus: New Research Forum series explores bold ideas in the era of AI; LASER improves reasoning in language models; Cache-Efficient Top-k Aggregation over High Cardinality Large Datasets; Six Microsoft researchers named 2023 ACM Fellows.<\/p>\n","protected":false},"author":42183,"featured_media":1004565,"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":"","footnotes":""},"categories":[1],"tags":[],"research-area":[13556,13563],"msr-region":[],"msr-event-type":[],"msr-locale":[268875],"msr-post-option":[243984],"msr-impact-theme":[],"msr-promo-type":[],"msr-podcast-series":[],"class_list":["post-1004556","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-research-blog","msr-research-area-artificial-intelligence","msr-research-area-data-platform-analytics","msr-locale-en_us","msr-post-option-blog-homepage-featured"],"msr_event_details":{"start":"","end":"","location":""},"podcast_url":"","podcast_episode":"","msr_research_lab":[851467,992148],"msr_impact_theme":[],"related-publications":[],"related-downloads":[],"related-videos":[],"related-academic-programs":[],"related-groups":[],"related-projects":[],"related-events":[999423,999417,999411,999201],"related-researchers":[{"type":"user_nicename","value":"Jordan Ash","user_id":39826,"display_name":"Jordan Ash","author_link":"<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/joash\/\" aria-label=\"Visit the profile page for Jordan Ash\">Jordan Ash<\/a>","is_active":false,"last_first":"Ash, Jordan","people_section":0,"alias":"joash"},{"type":"user_nicename","value":"Tarique Siddiqui","user_id":39645,"display_name":"Tarique Siddiqui","author_link":"<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/tasidd\/\" aria-label=\"Visit the profile page for Tarique Siddiqui\">Tarique Siddiqui<\/a>","is_active":false,"last_first":"Siddiqui, Tarique","people_section":0,"alias":"tasidd"},{"type":"user_nicename","value":"Vivek Narasayya","user_id":34602,"display_name":"Vivek Narasayya","author_link":"<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/viveknar\/\" aria-label=\"Visit the profile page for Vivek Narasayya\">Vivek Narasayya<\/a>","is_active":false,"last_first":"Narasayya, Vivek","people_section":0,"alias":"viveknar"},{"type":"user_nicename","value":"Surajit Chaudhuri","user_id":33764,"display_name":"Surajit Chaudhuri","author_link":"<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/surajitc\/\" aria-label=\"Visit the profile page for Surajit Chaudhuri\">Surajit Chaudhuri<\/a>","is_active":false,"last_first":"Chaudhuri, Surajit","people_section":0,"alias":"surajitc"},{"type":"user_nicename","value":"Jianfeng Gao","user_id":32246,"display_name":"Jianfeng Gao","author_link":"<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/jfgao\/\" aria-label=\"Visit the profile page for Jianfeng Gao\">Jianfeng Gao<\/a>","is_active":false,"last_first":"Gao, Jianfeng","people_section":0,"alias":"jfgao"},{"type":"user_nicename","value":"Sumit Gulwani","user_id":33755,"display_name":"Sumit Gulwani","author_link":"<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/sumitg\/\" aria-label=\"Visit the profile page for Sumit Gulwani\">Sumit Gulwani<\/a>","is_active":false,"last_first":"Gulwani, Sumit","people_section":0,"alias":"sumitg"},{"type":"user_nicename","value":"Nicole Immorlica","user_id":33086,"display_name":"Nicole Immorlica","author_link":"<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/nicimm\/\" aria-label=\"Visit the profile page for Nicole Immorlica\">Nicole Immorlica<\/a>","is_active":false,"last_first":"Immorlica, Nicole","people_section":0,"alias":"nicimm"},{"type":"user_nicename","value":"Stefan Saroiu","user_id":33716,"display_name":"Stefan Saroiu","author_link":"<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/ssaroiu\/\" aria-label=\"Visit the profile page for Stefan Saroiu\">Stefan Saroiu<\/a>","is_active":false,"last_first":"Saroiu, Stefan","people_section":0,"alias":"ssaroiu"},{"type":"user_nicename","value":"Manik Varma","user_id":32791,"display_name":"Manik Varma","author_link":"<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/manik\/\" aria-label=\"Visit the profile page for Manik Varma\">Manik Varma<\/a>","is_active":false,"last_first":"Varma, Manik","people_section":0,"alias":"manik"},{"type":"user_nicename","value":"Xing Xie","user_id":34906,"display_name":"Xing Xie","author_link":"<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/xingx\/\" aria-label=\"Visit the profile page for Xing Xie\">Xing Xie<\/a>","is_active":false,"last_first":"Xie, Xing","people_section":0,"alias":"xingx"}],"msr_type":"Post","featured_image_thumbnail":"<img width=\"960\" height=\"540\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/02\/RF34-BlogHeroFeature-1400x788-1-960x540.png\" class=\"img-object-cover\" alt=\"Research Focus Week of February 5, 2024\" decoding=\"async\" loading=\"lazy\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/02\/RF34-BlogHeroFeature-1400x788-1-960x540.png 960w, 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https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/02\/RF34-BlogHeroFeature-1400x788-1-1280x720.png 1280w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/02\/RF34-BlogHeroFeature-1400x788-1.png 1400w\" sizes=\"(max-width: 960px) 100vw, 960px\" \/>","byline":"","formattedDate":"February 7, 2024","formattedExcerpt":"Research Focus: New Research Forum series explores bold ideas in the era of AI; LASER improves reasoning in language models; Cache-Efficient Top-k Aggregation over High Cardinality Large Datasets; Six Microsoft researchers named 2023 ACM 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