{"id":31043,"date":"2023-05-11T14:38:42","date_gmt":"2023-05-11T18:38:42","guid":{"rendered":"https:\/\/www.bloomberg.com\/company\/stories\/humans-in-the-loop-ai-machine-learning-in-the-bloomberg-terminal\/"},"modified":"2024-10-04T14:35:46","modified_gmt":"2024-10-04T18:35:46","slug":"humans-in-the-loop-ai-machine-learning-in-the-bloomberg-terminal","status":"publish","type":"post","link":"https:\/\/www.bloomberg.com\/company\/stories\/humans-in-the-loop-ai-machine-learning-in-the-bloomberg-terminal\/","title":{"rendered":"Humans in the Loop: AI &amp; Machine Learning in the Bloomberg Terminal"},"content":{"rendered":"<div class='bbg-row bbg-bg--white  bbg-row--margin-top-none' data-anchor='row-6a076fee43c4e'>\n  \n\t\n\t\n\t<div class=\"bbg-row--content\">\n\t\t\n\t\t\t<div class='bbg-column bbg-column--width-8 bbg-column--offset-2'>\n\t<div class='bb-wysiwyg'>\n    \n    <p>Bloomberg processes over 200 million pieces of financial information every day. That\u2019s a lot of data, and to make it useful, AI and machine learning (ML) are playing an increasingly central role in the Terminal\u2019s ongoing evolution.<\/p>\n<p>Machine learning is about scouring data at speed and scale that is far beyond what human analysts can do. Then, the patterns or anomalies that are discovered can be used to derive powerful insights and guide the automation of all kinds of arduous or tedious tasks that humans used to have to perform manually.<\/p>\n<p>While AI continues to fall short of human intelligence in many applications, there are areas where it vastly outshines the performance of human agents. Machines can identify trends and patterns hidden across millions of documents, and this ability improves over time. Machines also behave consistently, in an unbiased fashion, without committing the kinds of mistakes that humans inevitably make.<\/p>\n<p>\u201cHumans are good at doing things deliberately, but when we make a decision, we start from whole cloth,\u201d says Gideon Mann, Head of ML Product &amp; Research in Bloomberg\u2019s CTO Office. \u201cMachines execute the same way every time, so even if they make a mistake, they do so with the same error characteristic.\u201d<\/p>\n<p>The Bloomberg Terminal currently employs AI and ML techniques in several exciting ways, and we can expect this practice to expand rapidly in the coming years. The story begins some 15 years ago\u2026<\/p>\n\n<\/div>\n\n\n<\/div>\n\n\n\t\t\n\t<\/div>\n<\/div>\n<div class='bbg-row bbg-bg--black ' data-anchor='row-6a076fee45924'>\n  \n\t\n\t\n\t<div class=\"bbg-row--content\">\n\t\t\n\t\t\t<div class='bbg-column bbg-column--width-6'>\n\t<p><style>#bloomberg-video-6a076fee6e457 .bloomberg-video__wrapper { background-image: url(https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&type=webp&url=https:\/\/assets.bbhub.io\/company\/sites\/51\/2023\/04\/Bloomberg-uses-AI-to-crunch-data-freeing-our-experts-to-focus-on-insight-150x150.jpg); }@media (min-width: 150px) { #bloomberg-video-6a076fee6e457 .bloomberg-video__wrapper { background-image: url(https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&type=webp&url=https:\/\/assets.bbhub.io\/company\/sites\/51\/2023\/04\/Bloomberg-uses-AI-to-crunch-data-freeing-our-experts-to-focus-on-insight-300x169.jpg); } }@media (min-width: 300px) { #bloomberg-video-6a076fee6e457 .bloomberg-video__wrapper { background-image: url(https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&type=webp&url=https:\/\/assets.bbhub.io\/company\/sites\/51\/2023\/04\/Bloomberg-uses-AI-to-crunch-data-freeing-our-experts-to-focus-on-insight-768x434.jpg); } }@media (min-width: 768px) { #bloomberg-video-6a076fee6e457 .bloomberg-video__wrapper { background-image: url(https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&type=webp&url=https:\/\/assets.bbhub.io\/company\/sites\/51\/2023\/04\/Bloomberg-uses-AI-to-crunch-data-freeing-our-experts-to-focus-on-insight-1024x578.jpg); } }<\/style>\n<div class='bloomberg-video' data-aspect-ratio='16:9' data-service='vimeo-video' id='bloomberg-video-6a076fee6e457'>\n\t<div class='bloomberg-video__wrapper bloomberg-video__wrapper--core'>\n\n<a\n\tclass=\"bloomberg-video__link\"\n\thref=\"https:\/\/vimeo.com\/819643737\"\n\ttarget=\"_blank\"\n\trel=\"noopener\"\n\ttitle=\"\"\n\tdata-video-id=\"819643737\"\n\tdata-orientation=\"Horizontal\"\n\tdata-video-loop=\"0\"\n\tdata-video-title=\"Keeping Humans in the Loop\"\n\taria-label=\"Play Video\"\n><\/a>\n\t<\/div>\n<\/div>\n<div class='bb-wysiwyg'>\n    \n    <p class=\"wysiwyg-attribution\" style=\"margin-top: 16px!\">(1:17) We use NLP &amp; ML to identify data in text, tables, and charts and surface it for our customers.<\/p>\n\n<\/div>\n\n<\/div>\n<div class='bbg-column bbg-column--width-5 bbg-column--valign-middle bbg-column--offset-1'>\n\t<div class='bb-wysiwyg'>\n    \n    <h2>Keeping Humans in the Loop<\/h2>\n<p>When we started in the 80s, data extraction was a manual process. Today, our engineers and data analysts build, train, and use AI to process unstructured data at massive speeds and scale \u2014 so our customers are in the know faster.<\/p>\n\n<\/div>\n\n\n<\/div>\n\n\t\t\n\t<\/div>\n<\/div>\n<div class='bbg-row bbg-bg--grey  bbg-row--margin-bottom-none' data-anchor='row-6a076fee79ab1'>\n  \n\t\n\t\n\t<div class=\"bbg-row--content\">\n\t\t\n\t\t\t<div class='bbg-column'>\n\t<div class='bb-wysiwyg'>\n    \n    <h2>Featured<\/h2>\n\n<\/div>\n\n\n<\/div>\n\n\n\t\t\n\t<\/div>\n<\/div>\n<div class='bbg-row bbg-bg--grey  bbg-row--margin-top-none' data-anchor='row-6a076fee7b131'>\n  \n\t\n\t\n\t<div class=\"bbg-row--content\">\n\t\t\n\t\t\t<div class='bbg-column bbg-column--width-1 bbg-column--m-width-2 bbg-column--s-width-2'>\n\t\n<\/div>\n<div class='bbg-column bbg-column--width-2 bbg-column--m-width-2 bbg-column--s-width-2'>\n\t<p><figure class=\"image-figure\" data-animation=\"\">\n    <img loading=\"lazy\" decoding=\"async\" width=\"285\" height=\"285\" src=\"https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&amp;type=webp&amp;url=https:\/\/assets.bbhub.io\/company\/sites\/51\/2023\/04\/James-Hook-Head-of-Data-at-Bloomberg.jpg\" class=\"attachment-full size-full image-figure__image image-figure__image--primary\" alt=\"\" srcset=\"https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&amp;type=webp&amp;url=https:\/\/assets.bbhub.io\/company\/sites\/51\/2023\/04\/James-Hook-Head-of-Data-at-Bloomberg.jpg 285w, https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&amp;type=webp&amp;url=https:\/\/assets.bbhub.io\/company\/sites\/51\/2023\/04\/James-Hook-Head-of-Data-at-Bloomberg.jpg 150w, https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&amp;type=webp&amp;url=https:\/\/assets.bbhub.io\/company\/sites\/51\/2023\/04\/James-Hook-Head-of-Data-at-Bloomberg.jpg 190w\" sizes=\"(max-width: 285px) 100vw, 285px\" \/>\n    \n<\/figure>\n<div class='bb-wysiwyg'>\n    \n    <p class=\"wysiwyg-attribution\" style=\"margin-top: -16px\"><a href=\"https:\/\/www.linkedin.com\/in\/james-hook-308a4566\" target=\"_blank\" rel=\"noopener noreferrer\"><strong>James Hook<\/strong><\/a><br \/>\nHead of Data<\/p>\n\n<\/div>\n\n<\/div>\n<div class='bbg-column bbg-column--width-2 bbg-column--m-width-2 bbg-column--s-width-2'>\n\t<p><figure class=\"image-figure\" data-animation=\"\">\n    <img loading=\"lazy\" decoding=\"async\" width=\"285\" height=\"285\" src=\"https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&amp;type=webp&amp;url=https:\/\/assets.bbhub.io\/company\/sites\/51\/2023\/04\/Anju-Kambadur-Head-of-AI-Engineering-at-Bloomberg.jpg\" class=\"attachment-full size-full image-figure__image image-figure__image--primary\" alt=\"\" srcset=\"https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&amp;type=webp&amp;url=https:\/\/assets.bbhub.io\/company\/sites\/51\/2023\/04\/Anju-Kambadur-Head-of-AI-Engineering-at-Bloomberg.jpg 285w, https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&amp;type=webp&amp;url=https:\/\/assets.bbhub.io\/company\/sites\/51\/2023\/04\/Anju-Kambadur-Head-of-AI-Engineering-at-Bloomberg.jpg 150w, https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&amp;type=webp&amp;url=https:\/\/assets.bbhub.io\/company\/sites\/51\/2023\/04\/Anju-Kambadur-Head-of-AI-Engineering-at-Bloomberg.jpg 190w\" sizes=\"(max-width: 285px) 100vw, 285px\" \/>\n    \n<\/figure>\n<div class='bb-wysiwyg'>\n    \n    <p class=\"wysiwyg-attribution\" style=\"margin-top: -16px\"><a href=\"https:\/\/www.linkedin.com\/in\/anju-kambadur-48aa78162\" target=\"_blank\" rel=\"noopener noreferrer\"><strong>Anju Kambadur<\/strong><\/a><br \/>\nHead of AI Engineering<\/p>\n\n<\/div>\n\n<\/div>\n<div class='bbg-column bbg-column--width-2 bbg-column--m-width-2 bbg-column--s-width-2'>\n\t<p><figure class=\"image-figure\" data-animation=\"\">\n    <img loading=\"lazy\" decoding=\"async\" width=\"285\" height=\"285\" src=\"https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&amp;type=webp&amp;url=https:\/\/assets.bbhub.io\/company\/sites\/51\/2023\/04\/Tina-Tseng-Data-Annotation-Strategist-at-Bloomberg.jpg\" class=\"attachment-full size-full image-figure__image image-figure__image--primary\" alt=\"\" srcset=\"https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&amp;type=webp&amp;url=https:\/\/assets.bbhub.io\/company\/sites\/51\/2023\/04\/Tina-Tseng-Data-Annotation-Strategist-at-Bloomberg.jpg 285w, https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&amp;type=webp&amp;url=https:\/\/assets.bbhub.io\/company\/sites\/51\/2023\/04\/Tina-Tseng-Data-Annotation-Strategist-at-Bloomberg.jpg 150w, https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&amp;type=webp&amp;url=https:\/\/assets.bbhub.io\/company\/sites\/51\/2023\/04\/Tina-Tseng-Data-Annotation-Strategist-at-Bloomberg.jpg 190w\" sizes=\"(max-width: 285px) 100vw, 285px\" \/>\n    \n<\/figure>\n<div class='bb-wysiwyg'>\n    \n    <p class=\"wysiwyg-attribution\" style=\"margin-top: -16px\"><a href=\"https:\/\/www.bloomberg.com\/company\/stories\/bloombergs-global-data-cto-data-science-teams-publish-best-practices-for-data-annotation-projects\/\" rel=\"noopener\"><strong>Tina Tseng<\/strong><\/a><br \/>\nML\/AI Data Strategist<\/p>\n\n<\/div>\n\n<\/div>\n<div class='bbg-column bbg-column--width-2 bbg-column--m-width-2 bbg-column--s-width-2'>\n\t<p><figure class=\"image-figure\" data-animation=\"\">\n    <img loading=\"lazy\" decoding=\"async\" width=\"285\" height=\"285\" src=\"https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&amp;type=webp&amp;url=https:\/\/assets.bbhub.io\/company\/sites\/51\/2023\/04\/Ketevan-Tsereteli-Senior-Researcher-AI-Engingeering-at-Bloomberg.jpg\" class=\"attachment-full size-full image-figure__image image-figure__image--primary\" alt=\"\" srcset=\"https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&amp;type=webp&amp;url=https:\/\/assets.bbhub.io\/company\/sites\/51\/2023\/04\/Ketevan-Tsereteli-Senior-Researcher-AI-Engingeering-at-Bloomberg.jpg 285w, https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&amp;type=webp&amp;url=https:\/\/assets.bbhub.io\/company\/sites\/51\/2023\/04\/Ketevan-Tsereteli-Senior-Researcher-AI-Engingeering-at-Bloomberg.jpg 150w, https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&amp;type=webp&amp;url=https:\/\/assets.bbhub.io\/company\/sites\/51\/2023\/04\/Ketevan-Tsereteli-Senior-Researcher-AI-Engingeering-at-Bloomberg.jpg 190w\" sizes=\"(max-width: 285px) 100vw, 285px\" \/>\n    \n<\/figure>\n<div class='bb-wysiwyg'>\n    \n    <p class=\"wysiwyg-attribution\" style=\"margin-top: -16px\"><a href=\"https:\/\/www.linkedin.com\/in\/ktsereteli\" target=\"_blank\" rel=\"noopener noreferrer\"><strong>Ketevan Tsereteli<\/strong><\/a><br \/>\nSenior Researcher, AI Engineering<\/p>\n\n<\/div>\n\n<\/div>\n<div class='bbg-column bbg-column--width-2 bbg-column--m-width-2 bbg-column--s-width-2'>\n\t<p><figure class=\"image-figure\" data-animation=\"\">\n    <img loading=\"lazy\" decoding=\"async\" width=\"285\" height=\"285\" src=\"https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&amp;type=webp&amp;url=https:\/\/assets.bbhub.io\/company\/sites\/51\/2023\/04\/Tom-Laufer-Head-of-Data-Sourcing-at-Bloomberg.jpg\" class=\"attachment-full size-full image-figure__image image-figure__image--primary\" alt=\"\" srcset=\"https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&amp;type=webp&amp;url=https:\/\/assets.bbhub.io\/company\/sites\/51\/2023\/04\/Tom-Laufer-Head-of-Data-Sourcing-at-Bloomberg.jpg 285w, https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&amp;type=webp&amp;url=https:\/\/assets.bbhub.io\/company\/sites\/51\/2023\/04\/Tom-Laufer-Head-of-Data-Sourcing-at-Bloomberg.jpg 150w, https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&amp;type=webp&amp;url=https:\/\/assets.bbhub.io\/company\/sites\/51\/2023\/04\/Tom-Laufer-Head-of-Data-Sourcing-at-Bloomberg.jpg 190w\" sizes=\"(max-width: 285px) 100vw, 285px\" \/>\n    \n<\/figure>\n<div class='bb-wysiwyg'>\n    \n    <p class=\"wysiwyg-attribution\" style=\"margin-top: -16px\"><a href=\"https:\/\/www.linkedin.com\/in\/thomas-laufer-2418294\" target=\"_blank\" rel=\"noopener noreferrer\"><strong>Tom Laufer<\/strong><\/a><br \/>\nHead of Data Sourcing<\/p>\n\n<\/div>\n\n<\/div>\n<div class='bbg-column bbg-column--width-1'>\n\t\n<\/div>\n\n\t\t\n\t<\/div>\n<\/div>\n<div class='bbg-row bbg-bg--white ' data-anchor='row-6a076fee87b49'>\n  \n\t\n\t\n\t<div class=\"bbg-row--content\">\n\t\t\n\t\t\t<div class='bbg-column bbg-column--width-8 bbg-column--offset-2'>\n\t<div class='bb-wysiwyg'>\n    \n    <h2>The rise of the machines<\/h2>\n<p>Prior to the 2000s, all tasks related to data collection, analysis, and distribution at Bloomberg were performed manually, because the technology did not yet exist to automate them. The new millennium brought some low-level automation to the company\u2019s workflows, with the emergence of primitive models operating by a series of if-then rules coded by humans. As the decade came to a close, true ML took flight within the company. Under this new approach, humans annotate data in order to train a machine to make various associations based on their labels. The machine \u201clearns\u201d how to make decisions, guided by this training data, and produces ever more accurate results over time. This approach can scale dramatically beyond traditional rules-based programming.<\/p>\n<p>In the last decade, there has been an explosive growth in the use of ML applications within Bloomberg. According to James Hook, Head of the company\u2019s Data department, there are a number of broad applications for AI\/ML and data science within Bloomberg.<\/p>\n<p>One is information extraction, where computer vision and\/or natural language processing (NLP) algorithms are used to read unstructured documents \u2014 data that\u2019s arranged in a format that\u2019s typically difficult for machines to read \u2014 in order to extract semantic meaning from them. With these techniques, the Terminal can present insights to users that are drawn from video, audio, blog posts, tweets, and more.<\/p>\n<p>Anju Kambadur, Head of Bloomberg\u2019s AI Engineering group, explains how this works:<\/p>\n<p>\u201cIt typically starts by asking questions of every document. Let\u2019s say we have a press release. What are the entities mentioned in the document? Who are the executives involved? Who are the other companies they&#8217;re doing business with? Are there any supply chain relationships exposed in the document? Then, once you\u2019ve determined the entities, you need to measure the salience of the relationships between them and associate the content with specific topics. A document might be about electric vehicles, it might be about oil, it might be relevant to the U.S., it might be relevant to the APAC region \u2014 all of these are called &#8216;topic codes&#8217; and they&#8217;re assigned using machine learning.\u201d<\/p>\n<p>All of this information, and much more, can be extracted from unstructured documents using natural language processing models.<\/p>\n<p>Another area is quality control, where techniques like anomaly detection are used to spot problems with dataset accuracy, among other areas. Using anomaly detection methods, the Terminal can spot the potential for a hidden investment opportunity, or flag suspicious market activity. For example, if a financial analyst was to change their rating of a particular stock following the company\u2019s quarterly earnings announcement, anomaly detection would be able to provide context around whether this is considered a typical behavior, or whether this action is worthy of being presented to Bloomberg clients as a data point worth considering in an investment decision.<\/p>\n<p>And then there\u2019s insight generation, where AI\/ML is used to analyze large datasets and unlock investment signals that might not otherwise be observed. One example of this is using highly correlated data like credit card transactions to gain visibility into recent company performance and consumer trends. Another is analyzing and summarizing the millions of news stories that are ingested into the Bloomberg Terminal each day to understand the key questions and themes that are driving specific markets or economic sectors or trading volume in a specific company\u2019s securities.<\/p>\n<h2>Humans in the loop<\/h2>\n<p>When we think of machine intelligence, we imagine an unfeeling autonomous machine, cold and impartial. In reality, however, the practice of ML is very much a team effort between humans and machines. Humans, for now at least, still define ontologies and methodologies, and perform annotations and quality assurance tasks. Bloomberg has moved quickly to increase staff capacity to perform these tasks at scale. In this scenario, the machines aren\u2019t replacing human workers; they are simply shifting their workflows away from more tedious, repetitive tasks toward higher level strategic oversight.<\/p>\n<p>\u201cIt&#8217;s really a transfer of human skill from manually extracting data points to thinking about defining and creating workflows,\u201d says Mann.<\/p>\n<p>Ketevan Tsereteli, a Senior Researcher in Bloomberg Engineering\u2019s Artificial Intelligence (AI) group, explains how this transfer works in practice.<\/p>\n<p>\u201cPreviously, in the manual workflow, you might have a team of data analysts that would be trained to find mergers and acquisition news in press releases and to extract the relevant information. They would have a lot of domain expertise on how this information is reported across different regions. Today, these same people are instrumental in collecting and labeling this information, and providing feedback on an ML model\u2019s performance, pointing out where it made correct and incorrect assumptions. In this way, that domain expertise is gradually transferred from human to machine.\u201d<\/p>\n<p>Humans are required at every step to ensure the models are performing optimally and improving over time. It\u2019s a collaborative effort involving ML engineers who build the learning systems and underlying infrastructure, AI researchers and data scientists who design and implement workflows, and annotators \u2014 journalists and other subject matter experts \u2014 who collect and label training data and perform quality assurance.<\/p>\n<p>\u201cWe have thousands of analysts in our Data department who have deep subject matter expertise in areas that matter most to our clients, like finance, law, and government,\u201d explains ML\/AI Data Strategist Tina Tseng. \u201cThey not only understand the data in these areas, but also how the data is used by our customers. They work very closely with our engineers and data scientists to develop our automation solutions.\u201d<\/p>\n<p>Annotation is critical, not just for training models, but also for evaluating their performance.<\/p>\n<p>\u201cWe&#8217;ll annotate data as a truth set \u2014 what they call a \u201cgolden\u201d copy of the data,\u201d says Tseng. \u201cThe model&#8217;s outputs can be automatically compared to that evaluation set so that we can calculate statistics to quantify how well the model is performing. Evaluation sets are used in both supervised and unsupervised learning.\u201d<\/p>\n\n<\/div>\n\n\n<\/div>\n\n\n\t\t\n\t<\/div>\n<\/div>\n<div class='bbg-row bbg-bg--black ' data-anchor='row-6a076fee89bdf'>\n  \n\t\n\t\n\t<div class=\"bbg-row--content\">\n\t\t\n\t\t\t<div class='bbg-column bbg-column--width-8'>\n\t<div class='bb-wysiwyg'>\n    \n    <p class=\"emphasis\">Check out \u201c<a href=\"https:\/\/arxiv.org\/abs\/2009.11654\" target=\"_blank\" rel=\"noopener noreferrer\">Best Practices for Managing Data Annotation Projects<\/a>,\u201d a practical guide published by Bloomberg&#8217;s CTO Office and Data department about planning and implementing data annotation initiatives.<\/p>\n\n<\/div>\n<div class=\"bbg-cta bbg-cta--color-default bbg-cta--align-left bbg-cta--type-link\"><a class=\"bbg-button bbg-button--align-left bbg-button--size-medium\" href=\"https:\/\/arxiv.org\/abs\/2009.11654\" id=\"bbg-button-6a076fee8a682\" data-element=\"cta-button\" data-description=\"READ NOW\" target=\"_blank\" rel=\"noopener external\">READ NOW<\/a><\/div>\n\n<\/div>\n<div class='bbg-column bbg-column--width-3 bbg-column--offset-1'>\n\t<figure class=\"image-figure\" data-animation=\"\">\n    <a class='image-figure__link' href='https:\/\/arxiv.org\/abs\/2009.11654' target=\"_blank\" rel=\"noopener\"><img loading=\"lazy\" decoding=\"async\" width=\"850\" height=\"1100\" src=\"https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&amp;type=webp&amp;url=https:\/\/assets.bbhub.io\/company\/sites\/51\/2023\/04\/Best-Practices-for-Managing-Data-Annotation-Projects-cover.png\" class=\"attachment-full size-full image-figure__image image-figure__image--primary\" alt=\"\" srcset=\"https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&amp;type=webp&amp;url=https:\/\/assets.bbhub.io\/company\/sites\/51\/2023\/04\/Best-Practices-for-Managing-Data-Annotation-Projects-cover.png 850w, https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&amp;type=webp&amp;url=https:\/\/assets.bbhub.io\/company\/sites\/51\/2023\/04\/Best-Practices-for-Managing-Data-Annotation-Projects-cover.png 232w, https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&amp;type=webp&amp;url=https:\/\/assets.bbhub.io\/company\/sites\/51\/2023\/04\/Best-Practices-for-Managing-Data-Annotation-Projects-cover.png 791w, https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&amp;type=webp&amp;url=https:\/\/assets.bbhub.io\/company\/sites\/51\/2023\/04\/Best-Practices-for-Managing-Data-Annotation-Projects-cover.png 768w, https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&amp;type=webp&amp;url=https:\/\/assets.bbhub.io\/company\/sites\/51\/2023\/04\/Best-Practices-for-Managing-Data-Annotation-Projects-cover.png 147w\" sizes=\"(max-width: 850px) 100vw, 850px\" \/><\/a>\n    \n<\/figure>\n\n\n<\/div>\n\n\t\t\n\t<\/div>\n<\/div>\n<div class='bbg-row bbg-bg--white  bbg-row--margin-bottom-compact' data-anchor='row-6a076fee8dabe'>\n  \n\t\n\t\n\t<div class=\"bbg-row--content\">\n\t\t\n\t\t\t<div class='bbg-column bbg-column--width-8 bbg-column--offset-2'>\n\t<div class='bb-wysiwyg'>\n    \n    <p><span style=\"font-weight: 400\">One of the reasons human involvement is so important across the ML algorithm lifecycle is due to data drift, which can occur when the data used to test and validate models changes over time.<\/span><\/p>\n<p><span style=\"font-weight: 400\">Tom Laufer, Head of Data Sourcing in Bloomberg\u2019s Data department, explains how this can happen:\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">\u201cIf GE has a 6% movement in margin, that\u2019s a major occurrence, because it&#8217;s a massive, mature, and stable company. Now, take a fledgling pharmaceutical company that\u2019s just hitting the market. You might see an 80% margin change quarter-over-quarter. That might be normal. But, as that pharmaceutical company gets bigger and bigger, the tolerance for margin in this example needs to get tighter and tighter. You have to be constantly reviewing the company, its size and nature, to understand that the tolerance for swings in certain data points needs to be adjusted as the company matures.\u201d<\/span><\/p>\n<p><span style=\"font-weight: 400\">The nature of the data coming into the Terminal is outside Bloomberg\u2019s control, so the Terminal\u2019s engineers and data scientists must utilize the latest advances in ML, along with deep industry knowledge, to keep models as relevant as possible.<\/span><\/p>\n<p><span style=\"font-weight: 400\">Occasionally, anomalous behavior can\u2019t be explained by previously known and understood factors. In these cases, when dealing with a true anomaly, that data point can be surfaced to Terminal users as a notification or as breaking news.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">\u201cMost of the time, we repair the data,\u201d says Laufer. \u201cBut when it\u2019s actually a true anomaly, we send that right off to the journalist who covers that company and say, \u2018This is newsworthy. You should write about this.\u2019 And we help them do that.\u201d<\/span><\/p>\n<h2>Deep domain knowledge<\/h2>\n<p><span style=\"font-weight: 400\">To understand how engineering and domain expertise work together, we can look at Bloomberg Law, a news and information platform for attorneys and other legal professionals. ML is used to identify and aggregate relevant information from sources like legal briefs, contracts, court\u00a0 opinions, and dockets. Traditional legal research is a time-consuming process that relies heavily on a lot of manual keyword searching of primary sources, as well as references to editorially-created secondary sources.<\/span><\/p>\n<p><span style=\"font-weight: 400\">\u201cManual keyword searching of primary sources involves a lot of perseverance and luck, in terms of determining the universe of keywords that could potentially retrieve relevant results,\u201d says Tseng. \u201cAnd reliance on editorially-created secondary sources is equally fraught with a lot of uncertainty and risk, as it places a lot of dependence on third-party editors who could be providing an incomplete or biased view of the data.\u201d<\/span><\/p>\n<p><span style=\"font-weight: 400\">Bloomberg uses ML to identify legally-significant language from millions of court opinions and aggregates that language so it&#8217;s searchable and discoverable by customers.<\/span><\/p>\n<p><span style=\"font-weight: 400\">\u201cWe use ML to identify features within court opinions, like citations, that indicate a statement&#8217;s salience,\u201d says Tseng. \u201cWe also use ML to parse those citations to find other court opinions that express the same legal concept and determine whether statements are semantically similar to each other in a legal context. This enables our customers to see trends they wouldn&#8217;t ordinarily see. For example, they may be able to determine the most persuasive language to use in their legal arguments on behalf of their clients based on how often that language is used or cited by judges in their rulings.\u201d\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">Tseng compares traditional research without ML to an impressionist painting made up of many individual dots. With ML, you can \u201cconnect\u201d those dots to get a higher resolution image of the data, allowing you to spot imagery in the painting that would otherwise not have been visible.<\/span><\/p>\n<p><span style=\"font-weight: 400\">\u201cWe&#8217;re trying to connect those dots for our customers, so they can make educated, data-driven decisions based on an aggregation of data, rather than anecdotal data,\u201d says Tseng.\u00a0<\/span><\/p>\n\n<\/div>\n\n\n<\/div>\n\n\n\t\t\n\t<\/div>\n<\/div>\n<div class='bbg-row bbg-bg--white  bbg-row--margin-top-none bbg-row--margin-bottom-none' data-anchor='row-6a076fee8f7b4'>\n  \n\t\n\t\n\t<div class=\"bbg-row--content\">\n\t\t\n\t\t\t<div class='bbg-column'>\n\t<div class='interstitial' data-element='interstitial-component'>\n\t<div class='interstitial-blue_border_design interstitial-bg-white'>\n\t\t<div class='interstitial-blue_border_design__rest bbg-column--width-7'>\n\t\t\t<p class='interstitial-blue_border_design__the_title interstitial_title'>Make it happen here.<\/p>\n\t\t\t<p class='interstitial-blue_border_design__text interstitial_text'><\/p>\n\t\t<\/div>\n\t\t<a class='interstitial-blue_border_design__link interstitial__link bbg-column--width-3 bbg-button bbg-button--size-large' href='https:\/\/bloomberg.avature.net\/en_US\/careers\/SearchJobs?utm_medium=mktg_site&utm_content=company_interstitial&utm_source=website' target='_blank' rel='noopener noreferrer' data-element='interstitial' data-description='' data-label='SEARCH NOW' data-element-position='[@data-element-position]'>SEARCH NOW<\/a>\n\t<\/div>\n<\/div>\n\n<\/div>\n\n\n\t\t\n\t<\/div>\n<\/div>\n<div class='bbg-row bbg-bg--white  bbg-row--margin-top-compact' data-anchor='row-6a076fee90f4f'>\n  \n\t\n\t\n\t<div class=\"bbg-row--content\">\n\t\t\n\t\t\t<div class='bbg-column bbg-column--width-8 bbg-column--offset-2'>\n\t<div class='bb-wysiwyg'>\n    \n    <h2>Speed and scale at the frontier<\/h2>\n<p><span style=\"font-weight: 400\">ML models are most often used in tandem with traditional rules-based programming.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">\u201cWe start with the problem and then find the right tools to solve the problem, versus starting with the tool and then finding where we can apply it,\u201d says Hook. \u201cWe try to use whichever tool is most effective to increase our automation rates, performance, and speed, and that improves our ability to cover as much as possible. But it&#8217;s not a one-size-fits-all-approach.\u201d<\/span><\/p>\n<p><span style=\"font-weight: 400\">Iterative implementation of the latest ML techniques have led to massive improvements in the speed and scale at which data can be transformed into insights.<\/span><\/p>\n<p><span style=\"font-weight: 400\">\u201cThe time between when a document is published to when it&#8217;s available to clients has, in some cases, been reduced from months to minutes,\u201d says Mann. \u201cIt used to be that one person&#8217;s reach was limited by the number of hours in the day that they could be looking at documents. Now, if you can set up and describe your test precisely, you can set it up for many different tests. The coverage and the amount of data that is available has dramatically changed, and the ability for our domain experts to set up these systems and deploy them is really phenomenal.\u201d<\/span><\/p>\n<p><span style=\"font-weight: 400\">\u201cThe best example we have of this is our MODL &lt;GO&gt; function,\u201d says Hook. \u201cIt shows you analysts\u2019 estimates for a company\u2019s financial performance, along with the company-reported numbers and the differences between the actuals and estimates. Five years ago we were looking at\u00a0 a handful of numbers per company and it would take six to 24 hours to input company-reported numbers into our system. We&#8217;re now comparing hundreds of financial metrics per company, and it takes less than a minute to ingest.\u201d<\/span><\/p>\n<p><span style=\"font-weight: 400\">As we discover more about how machines learn, machines are better able to understand how we think, too.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">\u201cOne frontier is enabling clients and our own internal experts to ask more flexible and ad hoc questions,\u201d says Mann. The other, he explains, is enabling machines to reason across data sets.<\/span><\/p>\n<p><span style=\"font-weight: 400\">\u201cHow can you use data that might have been effective for one task and transfer what you&#8217;ve learned in that task?\u201d asks Tsereteli<\/span><b>.<\/b><span style=\"font-weight: 400\"> \u201cLet&#8217;s say we&#8217;ve built a model for understanding when a news story talks about when a CEO is stepping down. Can we use the information the model has learned to make it easier to determine whether there&#8217;s going to be a merger or acquisition?\u201d<\/span><\/p>\n<p><span style=\"font-weight: 400\">\u201cWhen you can combine datasets easily, you can detect insights across them,\u201d notes Hook. \u201cWe&#8217;re looking to improve and invest in that, and I think it&#8217;s going to allow our aggregate data to be greater than the sum of its parts.\u201d<\/span><\/p>\n\n<\/div>\n\n\n<\/div>\n\n\n\t\t\n\t<\/div>\n<\/div>\n<div class='bbg-row bbg-bg--white  bbg-row--margin-top-none' data-anchor='row-6a076fee92e8a'>\n  \n\t\n\t\n\t<div class=\"bbg-row--content\">\n\t\t\n\t\t\t<div class='bbg-column bbg-column--width-8 bbg-column--halign-center bbg-column--offset-2'>\n\t<div class='bb-wysiwyg'>\n    \n    <p>More from this series<\/p>\n\n<\/div>\n<div class=\"bbg-arrow-cta-box bbg-arrow-cta-box--single-column\" style=\"--cta-count: 3;\"><div class=\"bbg-cta bbg-cta--color-default bbg-cta--align-center bbg-cta--type-arrow\"><a class=\"bbg-cta__arrow\" href=\"https:\/\/www.bloomberg.com\/company\/stories\/innovating-a-modern-icon-how-bloomberg-keeps-the-terminal-cutting-edge\/\" id=\"bbg-button-6a076fee93a5d\" data-element=\"cta-arrow\" data-description=\"&lt;span class=&#039;bbg-cta__arrow__header&#039;&gt;Learn how Bloomberg keeps the Terminal cutting-edge&lt;\/span&gt;\" ><span>Learn how Bloomberg keeps the Terminal cutting-edge<\/span><\/a><\/div><div class=\"bbg-cta bbg-cta--color-default bbg-cta--align-center bbg-cta--type-arrow\"><a class=\"bbg-cta__arrow\" href=\"https:\/\/www.bloomberg.com\/company\/stories\/how-bloomberg-handles-a-massive-wave-of-real-time-market-data-in-microseconds\/\" id=\"bbg-button-6a076fee93f57\" data-element=\"cta-arrow\" data-description=\"&lt;span class=&#039;bbg-cta__arrow__header&#039;&gt;Learn about the massive wave of data we process every day&lt;\/span&gt;\" ><span>Learn about the massive wave of data we process every day<\/span><\/a><\/div><div class=\"bbg-cta bbg-cta--color-default bbg-cta--align-center bbg-cta--type-arrow\"><a class=\"bbg-cta__arrow\" href=\"https:\/\/www.bloomberg.com\/company\/stories\/how-bloomberg-terminal-ux-designers-conceal-complexity\/\" id=\"bbg-button-6a076fee94388\" data-element=\"cta-arrow\" data-description=\"&lt;span class=&#039;bbg-cta__arrow__header&#039;&gt;Learn how Bloomberg UX designers conceal complexity&lt;\/span&gt;\" ><span>Learn how Bloomberg UX designers conceal complexity<\/span><\/a><\/div><\/div>\n\n<\/div>\n\n\n\t\t\n\t<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>The Bloomberg Terminal currently employs AI and ML techniques in several exciting ways, and we can expect this practice to expand rapidly in the coming years.<\/p>\n","protected":false},"author":375,"featured_media":31069,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1466],"tags":[1498,2060,1631,2056,1578,1788,1418,1676,2054,2055,2057,2059,2061,2053,1485,1624,2064,2052,1486,1580,1477,1497,2063,2062],"class_list":["post-31043","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-tech-at-bloomberg","tag-ai","tag-ai-ml","tag-anju-kambadur","tag-anomaly","tag-artificial-intelligence","tag-bloomberg-terminal","tag-data","tag-data-analysis","tag-financial-data","tag-insights","tag-intelligence","tag-james-hook","tag-ketevan-tsereteli","tag-language","tag-machine-learning","tag-ml","tag-ml-models","tag-models","tag-natural-language-processing","tag-nlp","tag-research","tag-terminal","tag-tina-tseng","tag-tom-laufer"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v19.11 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Humans in the Loop: AI &amp; 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