{"id":283,"date":"2021-08-31T14:40:23","date_gmt":"2021-08-31T14:40:23","guid":{"rendered":"https:\/\/fde.cat\/?p=283"},"modified":"2021-08-31T14:40:23","modified_gmt":"2021-08-31T14:40:23","slug":"ai-research-to-production-with-einstein-reply-recommendations","status":"publish","type":"post","link":"https:\/\/fde.cat\/index.php\/2021\/08\/31\/ai-research-to-production-with-einstein-reply-recommendations\/","title":{"rendered":"AI Research to Production with Einstein Reply Recommendations"},"content":{"rendered":"

We all know that AI is here and it\u2019s quickly changing our lives. However, the impacts of AI are unevenly distributed and it favors those with \u201cmore data,\u201d leaving those with \u201cfew data\u201d behind. This runs counter to our Salesforce core values of Customer Success and Equality, so we set out to change\u00a0things.<\/p>\n

\"\"<\/figure>\n

\u201cEinstein Reply Recommendations<\/a>\u201d is a Service Cloud feature that helps agents quickly respond to customers with the correct answer to common questions, saving agents time and increasing customer satisfaction. In order to make this feature available to more customers, especially those with \u201cfew data,\u201d the Service Cloud Engineering team joined forces with the Salesforce AI Research<\/a> team to tackle the\u00a0problem.<\/p>\n

\"\"
Einstein Reply Recommendations<\/figcaption><\/figure>\n

Our journey began with BERT<\/a>, which is a pre-trained unsupervised natural language processing model from Google. We took BERT and trained it on nine human-human and multi-turn task-oriented datasets across over 60 domains, creating what we call TOD-BERT<\/a>, a \u201cTask Oriented Dialogue\u201d version. When you think about the nature of Service calls, it makes perfect sense; most customers don\u2019t call to \u201cchit chat,\u201d they call to accomplish a specific \u201ctask\u201d such as a return, an exchange, etc.<\/p>\n

\"\"<\/figure>\n

TOD-BERT is self-supervised with two objectives: masked-language modeling and response contrastive learning. The former helps TOD-BERT capture contextual information while predicting the masked tokens, and the latter encourages TOD-BERT to learn dialogue structure information and response similarity. TOD-BERT is what we\u2019re now using to give customers with \u201cfew data\u201d a huge boost. Now they can leverage all the power of a giant pre-trained task oriented model but with their own\u00a0data!<\/p>\n

\"\"<\/figure>\n

In addition to powering Customer Success, this is also helping the whole world move forward in AI though peer-reviewed academic research. We published a paper and are thrilled to say that it was accepted to EMNLP<\/a>, a world-leading Natural Language Processing AI conference. <\/p>\n

This is how we use the latest breakthroughs in AI and Natural Language Processing at Salesforce to introduce users to power Customer Success for everyone.<\/p>\n

Read more on Salesforce AI Research<\/a> and Service Cloud Reply recommendations<\/a>.<\/h4>\n

And a big thanks to Senior Research Scientist Jason Wu<\/a> for leading these\u00a0efforts!<\/p>\n

\"\"<\/p>\n

\n

AI Research to Production with Einstein Reply Recommendations<\/a> was originally published in Salesforce Engineering<\/a> on Medium, where people are continuing the conversation by highlighting and responding to this story.<\/p>\n

Read More<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

We all know that AI is here and it\u2019s quickly changing our lives. However, the impacts of AI are unevenly distributed and it favors those with \u201cmore data,\u201d leaving those with \u201cfew data\u201d behind. This runs counter to our Salesforce core values of Customer Success and Equality, so we set out to change\u00a0things. \u201cEinstein Reply… Continue reading AI Research to Production with Einstein Reply Recommendations<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"spay_email":"","footnotes":""},"categories":[7],"tags":[],"class_list":["post-283","post","type-post","status-publish","format-standard","hentry","category-technology","entry"],"jetpack_featured_media_url":"","jetpack-related-posts":[{"id":229,"url":"https:\/\/fde.cat\/index.php\/2021\/02\/02\/ml-lake-building-salesforces-data-platform-for-machine-learning\/","url_meta":{"origin":283,"position":0},"title":"ML Lake: Building Salesforce\u2019s Data Platform for Machine Learning","date":"February 2, 2021","format":false,"excerpt":"Salesforce uses machine learning to improve every aspect of its product suite. 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This platform integrates generative AI, data management, CRM capabilities, and trusted systems to provide businesses with\u2026","rel":"","context":"In "Technology"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":884,"url":"https:\/\/fde.cat\/index.php\/2024\/06\/21\/how-einstein-copilot-sharpens-large-language-model-outputs-and-redefines-ai-data-testing\/","url_meta":{"origin":283,"position":2},"title":"How Einstein Copilot Sharpens Large Language Model Outputs and Redefines AI Data Testing","date":"June 21, 2024","format":false,"excerpt":"In our \u201cEngineering Energizers\u201d Q&A series, we explore the paths of engineering leaders who have attained significant accomplishments in their respective fields. Today, we spotlight Armita Peymandoust, Senior Vice President of Software Engineering at Salesforce, who spearheads the development of Einstein Copilot, a conversational AI assistant for CRM that integrates\u2026","rel":"","context":"In "Technology"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":785,"url":"https:\/\/fde.cat\/index.php\/2023\/11\/07\/einstein-for-flow-bringing-ai-innovation-to-the-next-generation-of-automation\/","url_meta":{"origin":283,"position":3},"title":"Einstein for Flow: Bringing AI Innovation to the Next Generation of Automation","date":"November 7, 2023","format":false,"excerpt":"By Vera Vetter, Zeyuan Chen, Ran Xu, and Scott Nyberg In our \u201cEngineering Energizers\u201d Q&A series, we examine the professional journeys that have shaped Salesforce Engineering leaders. Meet Vera Vetter, Product Management Director for Salesforce AI Research and a co-Product Manager for Einstein for Flow, a game-changing AI product that\u2026","rel":"","context":"In "Technology"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":733,"url":"https:\/\/fde.cat\/index.php\/2023\/07\/11\/how-is-salesforce-einstein-optimizing-ai-classification-model-accuracy\/","url_meta":{"origin":283,"position":4},"title":"How is Salesforce Einstein Optimizing AI Classification Model Accuracy?","date":"July 11, 2023","format":false,"excerpt":"In our \u201cEngineering Energizers\u201d Q&A series, we examine the professional journeys that have shaped Salesforce Engineering leaders. Meet Matan Rabi, Senior Software Engineer on Salesforce Einstein\u2019s Machine Learning Observability Platform (MLOP) team. Matan and his team strive to optimize the accuracy of Einstein\u2019s AI classification models, empowering customers across industries\u2026","rel":"","context":"In "Technology"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":876,"url":"https:\/\/fde.cat\/index.php\/2024\/06\/10\/sales-clouds-ai-transformation-welcome-to-the-autonomous-selling-era\/","url_meta":{"origin":283,"position":5},"title":"Sales Cloud\u2019s AI Transformation: Welcome to the Autonomous Selling Era","date":"June 10, 2024","format":false,"excerpt":"In our enlightening \u201cEngineering Energizers\u201d Q&A series, we explore the transformative experiences of engineers who have pioneered advancements in their fields. Today, we meet Parul Jain, Vice President of Software Engineering at Salesforce, who steers AI innovations within Sales Cloud. Her team is dedicated to developing a fully autonomous selling\u2026","rel":"","context":"In "Technology"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]}],"_links":{"self":[{"href":"https:\/\/fde.cat\/index.php\/wp-json\/wp\/v2\/posts\/283","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/fde.cat\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/fde.cat\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/fde.cat\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/fde.cat\/index.php\/wp-json\/wp\/v2\/comments?post=283"}],"version-history":[{"count":1,"href":"https:\/\/fde.cat\/index.php\/wp-json\/wp\/v2\/posts\/283\/revisions"}],"predecessor-version":[{"id":427,"href":"https:\/\/fde.cat\/index.php\/wp-json\/wp\/v2\/posts\/283\/revisions\/427"}],"wp:attachment":[{"href":"https:\/\/fde.cat\/index.php\/wp-json\/wp\/v2\/media?parent=283"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/fde.cat\/index.php\/wp-json\/wp\/v2\/categories?post=283"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/fde.cat\/index.php\/wp-json\/wp\/v2\/tags?post=283"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}