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It reuses what it knows to solve new problems.<br> <br> For example, it can learn English vocabulary and create a poem from the words it processes.<br> <br> Your organization can use generative AI for various purposes, like chatbots, media creation, product development, and design.</p>","id":"collection-text-media#what-is-gen-ai-overview","mediaPosition":"left","mediaAltText":"Young business people working together on new project"},"metadata":{"tags":[]}}]},"metadata":{"auth":{},"testAttributes":{}},"context":{"page":{"pageUrl":"https://aws.amazon.com/what-is/generative-ai/"},"environment":{"stage":"prod","region":"us-east-1"},"sdkVersion":"1.0.129"},"refMap":{"manifest.js":"47864e0370","rt-text-media-collection.rtl.css":"4a56177a31","rt-text-media-collection.js":"549552b6f3","rt-text-media-collection.css.js":"b7f4f2a16e","rt-text-media-collection.css":"dbf6370bb5","rt-text-media-collection.rtl.css.js":"641e7324ee"},"settings":{"templateMappings":{"hyperlinkText":"hyperlinkText","hyperlinkUrl":"hyperlinkUrl","heading":"heading","mediaAltText":"mediaAltText","mediaPosition":"mediaPosition","mediaUrl":"mediaUrl","subheader":"subheader","bodyContent":"bodyContent","videoOverlayTitle":"videoOverlayTitle","videoThumbnailUrl":"videoThumbnailUrl","videoPlayButtonText":"videoPlayButtonText","dark":"dark"}}}</script> <div data-eb-tpl-root="" data-reactroot=""> <div> <div data-rg-theme="purple" data-rg-mode="light" data-style-count="0"> <div data-rg-lang="en" data-rg-locale="en-US" lang="en-US" dir="ltr"> <div data-rg-n="GridStack" class="gridStack_module_gridStack__35667094" style="--rg-gridstack-gap:var(--rg-margin-2)"> <div data-rg-n="TextMediaContent" data-rg-theme="blue" data-rg-mode="light" data-media-position="start" class="textmediacontent_module_textMediaContent__d28b7a4a textmediacontent_module_gridPadding__d28b7a4a grid_module_grid__590284f4 grid_module_fullWidth__590284f4 grid_module_vPadding__590284f4" style="--rg-grid-gap:var(--rg-margin-3)"> <div data-rg-n="Col" data-text-form="long" class="col_module_col__66940a30 col_module_colXs12__66940a30 col_module_colS12__66940a30 col_module_colM6__66940a30 col_module_colL6__66940a30 col_module_colXl6__66940a30 col_module_colXxl6__66940a30 textmediacontent_module_textContainer__d28b7a4a"> <h2 data-rg-n="HeadingText" class="basetext_module_text__61099e5e headingtext_module_heading__00ee2f90 textmediacontent_module_heading__d28b7a4a headingtext_module_size2__00ee2f90">What is Gen AI?</h2> <div data-rg-n="BodyText" class="basetext_module_text__61099e5e bodytext_module_body__ca32720c bodytext_module_size1__ca32720c"> <p>Generative artificial intelligence, also known as generative AI or gen AI for short, is a type of AI that can create new content and ideas, including conversations, stories, images, videos, and music. It can learn human language, programming languages, art, chemistry, biology, or any complex subject matter. It reuses what it knows to solve new problems.<br> <br> For example, it can learn English vocabulary and create a poem from the words it processes.<br> <br> Your organization can use generative AI for various purposes, like chatbots, media creation, product development, and design.</p> </div> </div> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_col1__66940a30 textmediacontent_module_spacer__d28b7a4a"></div> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_colXs12__66940a30 col_module_colS12__66940a30 col_module_colM5__66940a30 col_module_colL5__66940a30 col_module_colXl5__66940a30 col_module_colXxl5__66940a30 textmediacontent_module_mediaContainer__d28b7a4a"> <img class="image_module_image__0914b1c0" src="https://d1.awsstatic.com/products/generative-ai/AdobeStock_372138889-square.b6a13f6fb96c7712ab8ba340b463ad8f1657d455.jpg" alt="Young business people working together on new project" loading="lazy"> </div> </div> </div> </div> </div> </div> </div> </div> </div> <div data-eb-slot="tab-text-media-gen-ai-e" data-eb-slot-meta="{'version':'1.0','slotId':'tab-text-media-gen-ai-e','experienceId':'a7569809-5370-4110-ba5b-b74892c435d9','allowBlank':false,'hasAltExp':false,'isRTR':false,'filters':{'limit':7,'query':'(id \u003d \'tab-text-media-generative-ai-examples-header\' OR id \u003d \'tab-text-media-generative-ai-examples-1\' OR id \u003d \'tab-text-media-generative-ai-examples-2\' OR id \u003d \'tab-text-media-generative-ai-examples-3\' OR id \u003d \'tab-text-media-generative-ai-examples-4\' OR id \u003d \'tab-text-media-generative-ai-examples-5\')'}}"> <div data-eb-tpl-n="awsm-rt/rt-text-media-tabs" data-eb-tpl-v="1.0.0" data-eb-ce="" data-eb-c-scope="tab-text-media-gen-ai-e" data-eb-d-scope="DIRECTORIES" data-eb-locale="en-US" data-eb-369baad2="" data-eb-ssr-ce="" data-eb-tpl-ns="awsmRT" data-eb-hydrated="pending"> <script type="application/json">{"data":{"items":[{"fields":{"patternHeading":"Generative AI examples","id":"awt-content-topics#tab-text-media-generative-ai-examples-header","itemCustomSortOrder":"0","patternSubheading":"Generative AI has several use cases across industries"},"metadata":{"tags":[{"id":"GLOBAL#pattern#pattern-data","name":"pattern-data","namespaceId":"GLOBAL#pattern","description":"pattern-data","metadata":{}}]}},{"fields":{"patternBoolean2":"0","patternBoolean3":"0","itemCustomSortOrder":"1","itemLongLoc":"<p>Financial services companies use generative AI tools to serve their customers better while reducing costs:</p> \n<ul> \n <li>Financial institutions use chatbots to generate product recommendations and respond to customer inquiries, which improves overall customer service.</li> \n <li>Lending institutions speed up loan approvals for financially underserved markets, especially in developing nations.</li> \n <li>Banks quickly detect fraud in claims, credit cards, and loans.</li> \n <li>Investment firms use the power of generative AI to provide safe, personalized financial advice to their clients at a low cost.</li> \n</ul> \n<p><a href=\"https://aws.amazon.com/financial-services/generative-ai/\">Read more about generative AI in Financial Services on AWS</a></p>","itemMediaAltText":"Finance Pie Chart","itemOption":"right","itemMediaURL":"https://d1.awsstatic.com/device-a-industry-image-assets/pan-industry/pan-industry_1200/studio/Pan-Industry_Studio_277_1200.98c254a26cd597239cadffac460c1a6319f806c7.jpg","itemHeading":"Financial services","id":"awt-content-topics#tab-text-media-generative-ai-examples-1","itemTextLoc3":"Financial services","itemBoolean":"0","patternBoolean":"0"},"metadata":{"tags":[]}},{"fields":{"patternBoolean2":"0","patternBoolean3":"0","itemCustomSortOrder":"2","itemLongLoc":"<p>One of the most promising generative AI use cases is accelerating drug discovery and research. 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<div data-eb-tpl-root="" data-reactroot=""> <span data-eb-item-id="awt-content-topics#tab-text-media-generative-ai-examples-header"> <div data-rg-lang="en" data-rg-locale="en-US" lang="en-US" dir="ltr"> <div data-rg-theme="fuchsia" data-rg-mode="light" data-style-count="0"> <div data-rg-n="GridStack" class="templatecontainer_module_templateContainer__dd3d201a gridStack_module_gridStack__35667094"> <div data-rg-n="Grid" class="grid_module_grid__590284f4" style="--rg-grid-gap:var(--rg-margin-3)"> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_col12__66940a30"> <div class="templateHeader_module_templateHeader__b496fe91"> <div data-rg-n="Grid" class="grid_module_grid__590284f4 grid_module_noPadding__590284f4" style="--rg-grid-gap:var(--rg-margin-3)"> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_colXs12__66940a30 col_module_colS10__66940a30 col_module_colM8__66940a30 col_module_colL8__66940a30 col_module_colXl8__66940a30 col_module_colXxl8__66940a30"> <h2 data-rg-n="HeadingText" class="basetext_module_text__61099e5e headingtext_module_heading__00ee2f90 headingtext_module_size2__00ee2f90">Generative AI examples</h2> <p data-rg-n="SubheadingText" class="basetext_module_text__61099e5e subheadingtext_module_subheading__99e66897 subheadingtext_module_size2__99e66897">Generative AI has several use cases across industries</p> </div> </div> </div> </div> </div> <div data-rg-n="Grid" class="grid_module_grid__590284f4" style="--rg-grid-gap:var(--rg-margin-3)"> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_col12__66940a30"> <div data-rg-n="Grid" class="tabs_module_tabs__05d921ca grid_module_grid__590284f4 grid_module_noPadding__590284f4" style="--panel-width:100%;--scroller-offset:0;--rg-grid-gap:var(--rg-margin-3)"> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_col12__66940a30 tabs_module_wrapper__05d921ca"> <div class="tabs_module_tabsViewport__05d921ca"> <div> <div data-rg-theme="fuchsia" data-rg-mode="light" data-style-count="0"> <div class="tablist_module_tabList__515d0ebb"> <div role="presentation" aria-hidden="true" style="--tab-indicator-x:0px;--tab-indicator-width:0px" class="backgroundGradient_module_backgroundGradient__7e2959cb backgroundGradient_module_variantA__7e2959cb tablist_module_indicator__515d0ebb"></div> <ul role="tablist"> <li class="tablist_module_tab__515d0ebb" role="none presentation"><button role="tab" tabindex="0" aria-selected="true" id="tab-undefined-awt-content-topics#tab-text-media-generative-ai-examples-1" aria-controls="tabpanel-undefined-awt-content-topics#tab-text-media-generative-ai-examples-1" type="button" class="dropShadow_module_shadow__0e93b107 dropShadow_module_themed__0e93b107 dropShadow_module_onHover__0e93b107 dropShadow_module_pressable__0e93b107 dropShadow_module_disabled__0e93b107 buttonbase_module_buttonBase__efa86cab buttonbase_module_tertiary__efa86cab button_module_button__f561bac2"><span data-rg-n="UtilityText" class="basetext_module_text__61099e5e basetext_module_center__61099e5e utilitytext_module_utility__9ef276a8 button_module_content__f561bac2 utilitytext_module_size3__9ef276a8">Financial services</span></button></li> <li class="tablist_module_tab__515d0ebb" role="none presentation"><button role="tab" tabindex="-1" aria-selected="false" id="tab-undefined-awt-content-topics#tab-text-media-generative-ai-examples-2" aria-controls="tabpanel-undefined-awt-content-topics#tab-text-media-generative-ai-examples-2" type="button" class="dropShadow_module_shadow__0e93b107 dropShadow_module_themed__0e93b107 dropShadow_module_onHover__0e93b107 dropShadow_module_pressable__0e93b107 dropShadow_module_disabled__0e93b107 buttonbase_module_buttonBase__efa86cab buttonbase_module_tertiary__efa86cab button_module_button__f561bac2"><span data-rg-n="UtilityText" class="basetext_module_text__61099e5e basetext_module_center__61099e5e utilitytext_module_utility__9ef276a8 button_module_content__f561bac2 utilitytext_module_size3__9ef276a8">Healthcare and life sciences</span></button></li> <li class="tablist_module_tab__515d0ebb" role="none presentation"><button role="tab" tabindex="-1" aria-selected="false" id="tab-undefined-awt-content-topics#tab-text-media-generative-ai-examples-3" aria-controls="tabpanel-undefined-awt-content-topics#tab-text-media-generative-ai-examples-3" type="button" class="dropShadow_module_shadow__0e93b107 dropShadow_module_themed__0e93b107 dropShadow_module_onHover__0e93b107 dropShadow_module_pressable__0e93b107 dropShadow_module_disabled__0e93b107 buttonbase_module_buttonBase__efa86cab buttonbase_module_tertiary__efa86cab button_module_button__f561bac2"><span data-rg-n="UtilityText" class="basetext_module_text__61099e5e basetext_module_center__61099e5e utilitytext_module_utility__9ef276a8 button_module_content__f561bac2 utilitytext_module_size3__9ef276a8">Automotive and manufacturing</span></button></li> <li class="tablist_module_tab__515d0ebb" role="none presentation"><button role="tab" tabindex="-1" aria-selected="false" id="tab-undefined-awt-content-topics#tab-text-media-generative-ai-examples-5" aria-controls="tabpanel-undefined-awt-content-topics#tab-text-media-generative-ai-examples-5" type="button" class="dropShadow_module_shadow__0e93b107 dropShadow_module_themed__0e93b107 dropShadow_module_onHover__0e93b107 dropShadow_module_pressable__0e93b107 dropShadow_module_disabled__0e93b107 buttonbase_module_buttonBase__efa86cab buttonbase_module_tertiary__efa86cab button_module_button__f561bac2"><span data-rg-n="UtilityText" class="basetext_module_text__61099e5e basetext_module_center__61099e5e utilitytext_module_utility__9ef276a8 button_module_content__f561bac2 utilitytext_module_size3__9ef276a8">Telecommunication</span></button></li> <li class="tablist_module_tab__515d0ebb" role="none presentation"><button role="tab" tabindex="-1" aria-selected="false" id="tab-undefined-awt-content-topics#tab-text-media-generative-ai-examples-4" aria-controls="tabpanel-undefined-awt-content-topics#tab-text-media-generative-ai-examples-4" type="button" class="dropShadow_module_shadow__0e93b107 dropShadow_module_themed__0e93b107 dropShadow_module_onHover__0e93b107 dropShadow_module_pressable__0e93b107 dropShadow_module_disabled__0e93b107 buttonbase_module_buttonBase__efa86cab buttonbase_module_tertiary__efa86cab button_module_button__f561bac2"><span data-rg-n="UtilityText" class="basetext_module_text__61099e5e basetext_module_center__61099e5e utilitytext_module_utility__9ef276a8 button_module_content__f561bac2 utilitytext_module_size3__9ef276a8">Media and entertainment</span></button></li> </ul> </div> </div> </div> </div> </div> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_col12__66940a30 tabs_module_panels__05d921ca"> <div class="tabs_module_scroller__05d921ca"> <div class="tabs_module_tabPanel__05d921ca tabs_module_active__05d921ca" role="tabpanel" id="tabpanel-undefined-awt-content-topics#tab-text-media-generative-ai-examples-1" tabindex="0" aria-labelledby="tab-undefined-awt-content-topics#tab-text-media-generative-ai-examples-1"> <div data-rg-n="TextMediaContent" data-media-position="end" class="textmediacontent_module_textMediaContent__d28b7a4a grid_module_grid__590284f4 grid_module_fullWidth__590284f4 grid_module_noPadding__590284f4" style="--rg-grid-gap:var(--rg-margin-3)"> <div data-rg-n="Col" data-text-form="long" class="col_module_col__66940a30 col_module_colXs12__66940a30 col_module_colS12__66940a30 col_module_colM6__66940a30 col_module_colL6__66940a30 col_module_colXl6__66940a30 col_module_colXxl6__66940a30 textmediacontent_module_textContainer__d28b7a4a"> <h3 data-rg-n="HeadingText" class="basetext_module_text__61099e5e headingtext_module_heading__00ee2f90 textmediacontent_module_heading__d28b7a4a headingtext_module_size3__00ee2f90">Financial services</h3> <div data-rg-n="BodyText" class="basetext_module_text__61099e5e bodytext_module_body__ca32720c bodytext_module_size2__ca32720c"> <p>Financial services companies use generative AI tools to serve their customers better while reducing costs:</p> <ul> <li>Financial institutions use chatbots to generate product recommendations and respond to customer inquiries, which improves overall customer service.</li> <li>Lending institutions speed up loan approvals for financially underserved markets, especially in developing nations.</li> <li>Banks quickly detect fraud in claims, credit cards, and loans.</li> <li>Investment firms use the power of generative AI to provide safe, personalized financial advice to their clients at a low cost.</li> </ul> <p><a href="https://aws.amazon.com/financial-services/generative-ai/">Read more about generative AI in Financial Services on AWS</a></p> </div> </div> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_col1__66940a30 textmediacontent_module_spacer__d28b7a4a"></div> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_colXs12__66940a30 col_module_colS12__66940a30 col_module_colM5__66940a30 col_module_colL5__66940a30 col_module_colXl5__66940a30 col_module_colXxl5__66940a30 textmediacontent_module_mediaContainer__d28b7a4a"> <img class="image_module_image__0914b1c0" src="https://d1.awsstatic.com/device-a-industry-image-assets/pan-industry/pan-industry_1200/studio/Pan-Industry_Studio_277_1200.98c254a26cd597239cadffac460c1a6319f806c7.jpg" alt="Finance Pie Chart" loading="lazy"> </div> </div> </div> <div class="tabs_module_tabPanel__05d921ca" role="tabpanel" id="tabpanel-undefined-awt-content-topics#tab-text-media-generative-ai-examples-2" tabindex="-1" aria-labelledby="tab-undefined-awt-content-topics#tab-text-media-generative-ai-examples-2"> <div data-rg-n="TextMediaContent" data-media-position="end" class="textmediacontent_module_textMediaContent__d28b7a4a grid_module_grid__590284f4 grid_module_fullWidth__590284f4 grid_module_noPadding__590284f4" style="--rg-grid-gap:var(--rg-margin-3)"> <div data-rg-n="Col" data-text-form="long" class="col_module_col__66940a30 col_module_colXs12__66940a30 col_module_colS12__66940a30 col_module_colM6__66940a30 col_module_colL6__66940a30 col_module_colXl6__66940a30 col_module_colXxl6__66940a30 textmediacontent_module_textContainer__d28b7a4a"> <h3 data-rg-n="HeadingText" class="basetext_module_text__61099e5e headingtext_module_heading__00ee2f90 textmediacontent_module_heading__d28b7a4a headingtext_module_size3__00ee2f90">Healthcare and life sciences</h3> <div data-rg-n="BodyText" class="basetext_module_text__61099e5e bodytext_module_body__ca32720c bodytext_module_size2__ca32720c"> <p>One of the most promising generative AI use cases is accelerating drug discovery and research. Generative AI can create novel protein sequences with specific properties for designing antibodies, enzymes, vaccines, and gene therapy.</p> <p>Healthcare and life sciences companies use generative AI tools to design synthetic gene sequences for synthetic biology and metabolic engineering applications. For example, they can create new biosynthetic pathways or optimize gene expression for biomanufacturing purposes.</p> <p>Generative AI tools also create synthetic patient and healthcare data. This data can be useful for training AI models, simulating clinical trials, or studying rare diseases without access to large real-world datasets.</p> <p><a href="https://aws.amazon.com/health/gen-ai/">Read more about Generative AI in Healthcare & Life Sciences on AWS</a></p> </div> </div> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_col1__66940a30 textmediacontent_module_spacer__d28b7a4a"></div> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_colXs12__66940a30 col_module_colS12__66940a30 col_module_colM5__66940a30 col_module_colL5__66940a30 col_module_colXl5__66940a30 col_module_colXxl5__66940a30 textmediacontent_module_mediaContainer__d28b7a4a"> <img class="image_module_image__0914b1c0" src="https://d1.awsstatic.com/NPO%20Getting%20Started.7e43ec32fff02c2e2c0fd84dc31cdebc332ed160.jpg" alt="Hands around Globe" loading="lazy"> </div> </div> </div> <div class="tabs_module_tabPanel__05d921ca" role="tabpanel" id="tabpanel-undefined-awt-content-topics#tab-text-media-generative-ai-examples-3" tabindex="-1" aria-labelledby="tab-undefined-awt-content-topics#tab-text-media-generative-ai-examples-3"> <div data-rg-n="TextMediaContent" data-media-position="end" class="textmediacontent_module_textMediaContent__d28b7a4a grid_module_grid__590284f4 grid_module_fullWidth__590284f4 grid_module_noPadding__590284f4" style="--rg-grid-gap:var(--rg-margin-3)"> <div data-rg-n="Col" data-text-form="long" class="col_module_col__66940a30 col_module_colXs12__66940a30 col_module_colS12__66940a30 col_module_colM6__66940a30 col_module_colL6__66940a30 col_module_colXl6__66940a30 col_module_colXxl6__66940a30 textmediacontent_module_textContainer__d28b7a4a"> <h3 data-rg-n="HeadingText" class="basetext_module_text__61099e5e headingtext_module_heading__00ee2f90 textmediacontent_module_heading__d28b7a4a headingtext_module_size3__00ee2f90">Automotive and manufacturing</h3> <div data-rg-n="BodyText" class="basetext_module_text__61099e5e bodytext_module_body__ca32720c bodytext_module_size2__ca32720c"> <p>Automotive companies use generative AI technology for many purposes, from engineering to in-vehicle experiences and customer service. For instance, they optimize the design of mechanical parts to reduce drag in vehicle designs or adapt the design of personal assistants.</p> <p>Auto companies use generative AI tools to deliver better customer service by providing quick responses to the most common customer questions. Generative AI creates new materials, chips, and part designs to optimize manufacturing processes and reduce costs.</p> <p>Another generative AI use case is synthesizing data to test applications. This is especially helpful for data not often included in testing datasets (such as defects or edge cases).</p> <p><a href="https://aws.amazon.com/automotive/automotive-generative-ai/">Read more about Generative AI for Automotive on AWS</a></p> <p><a href="https://aws.amazon.com/industrial/generative-ai/">Read more about Generative AI in Manufacturing on AWS</a></p> </div> </div> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_col1__66940a30 textmediacontent_module_spacer__d28b7a4a"></div> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_colXs12__66940a30 col_module_colS12__66940a30 col_module_colM5__66940a30 col_module_colL5__66940a30 col_module_colXl5__66940a30 col_module_colXxl5__66940a30 textmediacontent_module_mediaContainer__d28b7a4a"> <img class="image_module_image__0914b1c0" src="https://d2908q01vomqb2.cloudfront.net/a17554a0d2b15a664c0e73900184544f19e70227/2024/09/23/3D-Model-1-300x245.png" alt="Automotive and manufacturing" loading="lazy"> </div> </div> </div> <div class="tabs_module_tabPanel__05d921ca" role="tabpanel" id="tabpanel-undefined-awt-content-topics#tab-text-media-generative-ai-examples-5" tabindex="-1" aria-labelledby="tab-undefined-awt-content-topics#tab-text-media-generative-ai-examples-5"> <div data-rg-n="TextMediaContent" data-media-position="end" class="textmediacontent_module_textMediaContent__d28b7a4a grid_module_grid__590284f4 grid_module_fullWidth__590284f4 grid_module_noPadding__590284f4" style="--rg-grid-gap:var(--rg-margin-3)"> <div data-rg-n="Col" data-text-form="long" class="col_module_col__66940a30 col_module_colXs12__66940a30 col_module_colS12__66940a30 col_module_colM6__66940a30 col_module_colL6__66940a30 col_module_colXl6__66940a30 col_module_colXxl6__66940a30 textmediacontent_module_textContainer__d28b7a4a"> <h3 data-rg-n="HeadingText" class="basetext_module_text__61099e5e headingtext_module_heading__00ee2f90 textmediacontent_module_heading__d28b7a4a headingtext_module_size3__00ee2f90">Telecommunication</h3> <div data-rg-n="BodyText" class="basetext_module_text__61099e5e bodytext_module_body__ca32720c bodytext_module_size2__ca32720c"> <p>Generative AI use cases in telecommunication focus on reinventing the customer experience defined by the cumulative interactions of subscribers across all touchpoints of the customer journey.</p> <p>For instance, telecommunication organizations apply generative AI to improve customer service with live human-like conversational agents. They reinvent customer relationships with personalized one-to-one sales assistants. They also optimize network performance by analyzing network data to recommend fixes. </p> <p><a href="https://aws.amazon.com/telecom/generative-ai/">Read more about Generative AI for Telecom on AWS</a></p> </div> </div> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_col1__66940a30 textmediacontent_module_spacer__d28b7a4a"></div> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_colXs12__66940a30 col_module_colS12__66940a30 col_module_colM5__66940a30 col_module_colL5__66940a30 col_module_colXl5__66940a30 col_module_colXxl5__66940a30 textmediacontent_module_mediaContainer__d28b7a4a"> <img class="image_module_image__0914b1c0" src="https://d1.awsstatic.com/industry/telecom/2023-site/transforming-telcos.077e03f55bd9bb9cc193165e533fd5bc40db0de0.jpg" alt="Telecommunication" loading="lazy"> </div> </div> </div> <div class="tabs_module_tabPanel__05d921ca" role="tabpanel" id="tabpanel-undefined-awt-content-topics#tab-text-media-generative-ai-examples-4" tabindex="-1" aria-labelledby="tab-undefined-awt-content-topics#tab-text-media-generative-ai-examples-4"> <div data-rg-n="TextMediaContent" data-media-position="end" class="textmediacontent_module_textMediaContent__d28b7a4a grid_module_grid__590284f4 grid_module_fullWidth__590284f4 grid_module_noPadding__590284f4" style="--rg-grid-gap:var(--rg-margin-3)"> <div data-rg-n="Col" data-text-form="long" class="col_module_col__66940a30 col_module_colXs12__66940a30 col_module_colS12__66940a30 col_module_colM6__66940a30 col_module_colL6__66940a30 col_module_colXl6__66940a30 col_module_colXxl6__66940a30 textmediacontent_module_textContainer__d28b7a4a"> <h3 data-rg-n="HeadingText" class="basetext_module_text__61099e5e headingtext_module_heading__00ee2f90 textmediacontent_module_heading__d28b7a4a headingtext_module_size3__00ee2f90">Media and entertainment</h3> <div data-rg-n="BodyText" class="basetext_module_text__61099e5e bodytext_module_body__ca32720c bodytext_module_size2__ca32720c"> <p>From animations and scripts to full-length movies, generative AI models produce novel content at a fraction of the cost and time of traditional production.</p> <p>Other generative AI use cases in the industry include:</p> <ul> <li>Artists can complement and enhance their albums with AI-generated music to create whole new experiences.</li> <li>Media organizations use generative AI to improve their audience experiences by offering personalized content and ads to grow revenues.</li> <li>Gaming companies use generative AI to create new games and allow players to build avatars.</li> </ul> </div> </div> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_col1__66940a30 textmediacontent_module_spacer__d28b7a4a"></div> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_colXs12__66940a30 col_module_colS12__66940a30 col_module_colM5__66940a30 col_module_colL5__66940a30 col_module_colXl5__66940a30 col_module_colXxl5__66940a30 textmediacontent_module_mediaContainer__d28b7a4a"> <img class="image_module_image__0914b1c0" src="https://d1.awsstatic.com/DynamoDB%20Media%20Entertainment.9678cf61afa4af5af6f80a080e1221a5c69268ea.jpg" alt="Media and entertainment" loading="lazy"> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div></span> </div> </div> </div> <div data-eb-slot="pdp-benefits-gen-ai" data-eb-slot-meta="{'version':'1.0','slotId':'pdp-benefits-gen-ai','experienceId':'5b0041ec-5bf3-11ee-8c99-0242ac120002','allowBlank':false,'hasAltExp':false,'isRTR':false,'filters':{'limit':7,'query':'(id \u003d \'generative-ai-benefits-heading\' OR id \u003d \'generative-ai-benefits-1\' OR id \u003d \'generative-ai-benefits-2\' OR id \u003d \'generative-ai-benefits-3\' OR id \u003d \'generative-ai-benefits-4\')'}}"> <div data-eb-tpl-n="awsm-rt/rt-qa-sampler" data-eb-tpl-v="1.0.0" data-eb-ce="" data-eb-c-scope="pdp-benefits-gen-ai" data-eb-d-scope="DIRECTORIES" data-eb-locale="en-US" data-eb-d9d0c036="" data-eb-ssr-ce="" data-eb-tpl-ns="awsmRT" data-eb-hydrated="pending"> <script type="application/json">{"data":{"items":[{"fields":{"id":"product-benefits#generative-ai-benefits-heading","customSortOrder":"0","patternTitle":"Generative AI benefits","subheading":"According to Goldman Sachs, generative AI could drive a 7 percent (or almost $7 trillion) increase in global gross domestic product (GDP) and lift productivity growth by 1.5 percentage points over ten years. 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They can increase customer engagement by presenting curated offers and communication in a personalized way."},"metadata":{"tags":[]}},{"fields":{"patternInlineHeading":"0","benefitTitle":"Optimizes business processes","id":"product-benefits#generative-ai-benefits-3","customSortOrder":"2","benefitContent":"<p>With generative AI, your business can optimize business processes utilizing machine learning (ML) and AI applications across all lines of business. You can apply the technology across all lines of business, including engineering, marketing, customer service, finance, and sales.</p> \n<p>For example, here's what generative AI can do for optimization:</p> \n<ul> \n <li>Extract and summarize data from any source for knowledge search functions.</li> \n <li>Evaluate and optimize different scenarios for cost reduction in areas like marketing, advertising, finance, and logistics.</li> \n <li>Generate synthetic data to create labeled data for supervised learning and other ML processes.</li> \n</ul>"},"metadata":{"tags":[]}},{"fields":{"patternInlineHeading":"0","benefitTitle":"Boosts employee productivity","id":"product-benefits#generative-ai-benefits-4","customSortOrder":"3","benefitContent":"<p>Generative AI models can augment employee workflows and act as efficient assistants for everyone in your organization. They can do everything from searching to creation in a human-like way. Generative AI can boost productivity for different kinds of workers:</p>      \n<ul>\n <li>Support creative tasks by generating multiple prototypes based on certain inputs and constraints. It can also optimize existing designs based on human feedback and specified constraints.</li>\n <li>Generate new software <a href=\"https://aws.amazon.com/what-is/ai-coding/\"> code suggestions </a> for application development tasks.</li>\n <li>Support management by generating reports, summaries, and projections.</li>\n <li>Generate new sales scripts, email content, and blogs for marketing teams.</li>\n</ul>\n<p>You can save time, reduce costs, and enhance efficiency across your organization.</p>"},"metadata":{"tags":[]}}]},"metadata":{"auth":{},"pagination":{"empty":false,"present":true},"testAttributes":{}},"context":{"page":{"pageUrl":"https://aws.amazon.com/what-is/generative-ai/"},"environment":{"stage":"prod","region":"us-east-1"},"sdkVersion":"1.0.129"},"refMap":{"manifest.js":"3daf852ae2","rt-qa-sampler.js":"a1a586a2a7","rt-qa-sampler.rtl.css":"bd7152f8b0","rt-qa-sampler.css":"c739e31c0a","rt-qa-sampler.css.js":"f965de4043","rt-qa-sampler.rtl.css.js":"ab040a2e3b"},"settings":{"templateMappings":{"patternHeading":"patternTitle","patternSubheading":"subheading","topic":"benefitTitle","content":"benefitContent"}}}</script> <div data-eb-tpl-root="" data-reactroot=""> <span data-eb-item-id="product-benefits#generative-ai-benefits-heading"> <div> <div data-rg-lang="en" data-rg-locale="en-US" lang="en-US" dir="ltr"> <div data-rg-theme="purple" data-rg-mode="light" data-style-count="0"> <div data-rg-n="GridStack" class="gridStack_module_gridStack__35667094"> <div data-rg-n="Grid" class="qaSampler_module_qaSamplerGrid__b180ba78 grid_module_grid__590284f4" style="--rg-grid-gap:var(--rg-margin-3)"> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_colXs12__66940a30 col_module_colS12__66940a30 col_module_colM5__66940a30 col_module_colL5__66940a30 col_module_colXl5__66940a30 col_module_colXxl5__66940a30"> <div data-testid="sticky-header" class="stickyHeader_module_stickyHeader__91e4d062" style="top:0px"> <h2 data-rg-n="HeadingText" class="basetext_module_text__61099e5e headingtext_module_heading__00ee2f90 headingtext_module_size2__00ee2f90">Generative AI benefits</h2> <h5 data-rg-n="SubheadingText" class="basetext_module_text__61099e5e subheadingtext_module_subheading__99e66897 subheadingtext_module_size2__99e66897">According to Goldman Sachs, generative AI could drive a 7 percent (or almost $7 trillion) increase in global gross domestic product (GDP) and lift productivity growth by 1.5 percentage points over ten years. Next, we give some more benefits of generative AI.</h5> </div> </div> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_colXs0__66940a30 col_module_colS0__66940a30 col_module_colM1__66940a30 col_module_colL1__66940a30 col_module_colXl1__66940a30 col_module_colXxl1__66940a30"></div> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_colXs12__66940a30 col_module_colS12__66940a30 col_module_colM6__66940a30 col_module_colL6__66940a30 col_module_colXl6__66940a30 col_module_colXxl6__66940a30 qaSampler_module_qaSamplerExpanderCol__b180ba78"> <div class="itemExpander_module_expandableSection__e56d1e98" data-eb-item-id="0"> <div class="itemExpander_module_expandableSectionHeader__e56d1e98 qaSampler_module_qaSamplerExpanderHeader__b180ba78" id="0-trigger" aria-controls="content-0" aria-expanded="false" tabindex="0" role="button" data-rigel-analytics="{"name":"ItemExpander","properties":{"expanded":false}}"> <h3 data-rg-n="TitleText" class="basetext_module_text__61099e5e titletext_module_title__e23b0698 qaSampler_module_qaSamplerTitle__b180ba78 titletext_module_size4__e23b0698">Accelerates research</h3> <div class="itemExpander_module_expandableSectionHeaderTrigger__e56d1e98" aria-hidden="true"> <span class="icon_module_icon__31e1c712 icon_module_sizeS__31e1c712"> <svg role="presentation" xmlns="http://www.w3.org/2000/svg" viewbox="0 0 16 16"> <path d="M16 9H9V16H7V9H0V7H7V0H9V7H16V9Z" data-type="fill"></path> </svg></span> </div> </div> <div id="content-0" aria-labelledby="0-trigger" class="itemExpander_module_expandableSectionContent__e56d1e98"> <div class="itemExpander_module_expandableSectionContentWrapper__e56d1e98"> <div data-rg-n="BodyText" class="basetext_module_text__61099e5e bodytext_module_body__ca32720c qaSampler_module_qaSamplerContent__b180ba78 bodytext_module_size1__ca32720c"> Generative AI algorithms can explore and analyze complex data in new ways, allowing researchers to discover new trends and patterns that may not be otherwise apparent. These algorithms can summarize content, outline multiple solution paths, brainstorm ideas, and create detailed documentation from research notes. This is why generative AI drastically enhances research and innovation. For example, generative AI systems are being used in the pharma industry to generate and optimize protein sequences and significantly accelerate drug discovery. </div> </div> </div> </div> <div class="itemExpander_module_expandableSection__e56d1e98" data-eb-item-id="1"> <div class="itemExpander_module_expandableSectionHeader__e56d1e98 qaSampler_module_qaSamplerExpanderHeader__b180ba78" id="1-trigger" aria-controls="content-1" aria-expanded="false" tabindex="0" role="button" data-rigel-analytics="{"name":"ItemExpander","properties":{"expanded":false}}"> <h3 data-rg-n="TitleText" class="basetext_module_text__61099e5e titletext_module_title__e23b0698 qaSampler_module_qaSamplerTitle__b180ba78 titletext_module_size4__e23b0698">Enhances customer experience</h3> <div class="itemExpander_module_expandableSectionHeaderTrigger__e56d1e98" aria-hidden="true"> <span class="icon_module_icon__31e1c712 icon_module_sizeS__31e1c712"> <svg role="presentation" xmlns="http://www.w3.org/2000/svg" viewbox="0 0 16 16"> <path d="M16 9H9V16H7V9H0V7H7V0H9V7H16V9Z" data-type="fill"></path> </svg></span> </div> </div> <div id="content-1" aria-labelledby="1-trigger" class="itemExpander_module_expandableSectionContent__e56d1e98"> <div class="itemExpander_module_expandableSectionContentWrapper__e56d1e98"> <div data-rg-n="BodyText" class="basetext_module_text__61099e5e bodytext_module_body__ca32720c qaSampler_module_qaSamplerContent__b180ba78 bodytext_module_size1__ca32720c"> Generative AI can respond naturally to human conversation and serve as a tool for customer service and personalization of customer workflows. For example, you can use AI-powered chatbots, voice bots, and virtual assistants that respond more accurately to customers for first-contact resolution. They can increase customer engagement by presenting curated offers and communication in a personalized way. </div> </div> </div> </div> <div class="itemExpander_module_expandableSection__e56d1e98" data-eb-item-id="2"> <div class="itemExpander_module_expandableSectionHeader__e56d1e98 qaSampler_module_qaSamplerExpanderHeader__b180ba78" id="2-trigger" aria-controls="content-2" aria-expanded="false" tabindex="0" role="button" data-rigel-analytics="{"name":"ItemExpander","properties":{"expanded":false}}"> <h3 data-rg-n="TitleText" class="basetext_module_text__61099e5e titletext_module_title__e23b0698 qaSampler_module_qaSamplerTitle__b180ba78 titletext_module_size4__e23b0698">Optimizes business processes</h3> <div class="itemExpander_module_expandableSectionHeaderTrigger__e56d1e98" aria-hidden="true"> <span class="icon_module_icon__31e1c712 icon_module_sizeS__31e1c712"> <svg role="presentation" xmlns="http://www.w3.org/2000/svg" viewbox="0 0 16 16"> <path d="M16 9H9V16H7V9H0V7H7V0H9V7H16V9Z" data-type="fill"></path> </svg></span> </div> </div> <div id="content-2" aria-labelledby="2-trigger" class="itemExpander_module_expandableSectionContent__e56d1e98"> <div class="itemExpander_module_expandableSectionContentWrapper__e56d1e98"> <div data-rg-n="BodyText" class="basetext_module_text__61099e5e bodytext_module_body__ca32720c qaSampler_module_qaSamplerContent__b180ba78 bodytext_module_size1__ca32720c"> <p>With generative AI, your business can optimize business processes utilizing machine learning (ML) and AI applications across all lines of business. You can apply the technology across all lines of business, including engineering, marketing, customer service, finance, and sales.</p> <p>For example, here's what generative AI can do for optimization:</p> <ul> <li>Extract and summarize data from any source for knowledge search functions.</li> <li>Evaluate and optimize different scenarios for cost reduction in areas like marketing, advertising, finance, and logistics.</li> <li>Generate synthetic data to create labeled data for supervised learning and other ML processes.</li> </ul> </div> </div> </div> </div> <div class="itemExpander_module_expandableSection__e56d1e98" data-eb-item-id="3"> <div class="itemExpander_module_expandableSectionHeader__e56d1e98 qaSampler_module_qaSamplerExpanderHeader__b180ba78" id="3-trigger" aria-controls="content-3" aria-expanded="false" tabindex="0" role="button" data-rigel-analytics="{"name":"ItemExpander","properties":{"expanded":false}}"> <h3 data-rg-n="TitleText" class="basetext_module_text__61099e5e titletext_module_title__e23b0698 qaSampler_module_qaSamplerTitle__b180ba78 titletext_module_size4__e23b0698">Boosts employee productivity</h3> <div class="itemExpander_module_expandableSectionHeaderTrigger__e56d1e98" aria-hidden="true"> <span class="icon_module_icon__31e1c712 icon_module_sizeS__31e1c712"> <svg role="presentation" xmlns="http://www.w3.org/2000/svg" viewbox="0 0 16 16"> <path d="M16 9H9V16H7V9H0V7H7V0H9V7H16V9Z" data-type="fill"></path> </svg></span> </div> </div> <div id="content-3" aria-labelledby="3-trigger" class="itemExpander_module_expandableSectionContent__e56d1e98"> <div class="itemExpander_module_expandableSectionContentWrapper__e56d1e98"> <div data-rg-n="BodyText" class="basetext_module_text__61099e5e bodytext_module_body__ca32720c qaSampler_module_qaSamplerContent__b180ba78 bodytext_module_size1__ca32720c"> <p>Generative AI models can augment employee workflows and act as efficient assistants for everyone in your organization. They can do everything from searching to creation in a human-like way. Generative AI can boost productivity for different kinds of workers:</p>       <ul> <li>Support creative tasks by generating multiple prototypes based on certain inputs and constraints. It can also optimize existing designs based on human feedback and specified constraints.</li> <li>Generate new software <a href="https://aws.amazon.com/what-is/ai-coding/"> code suggestions </a> for application development tasks.</li> <li>Support management by generating reports, summaries, and projections.</li> <li>Generate new sales scripts, email content, and blogs for marketing teams.</li> </ul> <p>You can save time, reduce costs, and enhance efficiency across your organization.</p> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div></span> </div> </div> </div> <div data-eb-slot="tab-text-media-gen-ai-evolve" data-eb-slot-meta="{'version':'1.0','slotId':'tab-text-media-gen-ai-evolve','experienceId':'a7569809-5370-4110-ba5b-b74892c435d9','allowBlank':false,'hasAltExp':false,'isRTR':false,'filters':{'limit':7,'query':'(id \u003d \'tab-text-media-how-generative-ai-evolved-header\' OR id \u003d \'tab-text-media-how-generative-ai-evolved-1\' OR id \u003d \'tab-text-media-how-generative-ai-evolved-2\' OR id \u003d \'tab-text-media-how-generative-ai-evolved-3\')'}}"> <div data-eb-tpl-n="awsm-rt/rt-text-media-tabs" data-eb-tpl-v="1.0.0" data-eb-ce="" data-eb-c-scope="tab-text-media-gen-ai-evolve" data-eb-d-scope="DIRECTORIES" data-eb-locale="en-US" data-eb-369baad2="" data-eb-ssr-ce="" data-eb-tpl-ns="awsmRT" data-eb-hydrated="pending"> <script type="application/json">{"data":{"items":[{"fields":{"patternHeading":"How did generative AI technology evolve?","id":"awt-content-topics#tab-text-media-how-generative-ai-evolved-header","itemCustomSortOrder":"0","patternSubheading":"Primitive generative models have been used for decades in statistics to aid in numerical data analysis. 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Neural networks and deep learning were recent precursors for modern generative AI. Variational autoencoders, developed in 2013, were the first deep generative models that could generate realistic images and speech.</p> </div> </div> </div> </div> </div> <div data-rg-n="Grid" class="grid_module_grid__590284f4" style="--rg-grid-gap:var(--rg-margin-3)"> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_col12__66940a30"> <div data-rg-n="Grid" class="tabs_module_tabs__05d921ca grid_module_grid__590284f4 grid_module_noPadding__590284f4" style="--panel-width:100%;--scroller-offset:0;--rg-grid-gap:var(--rg-margin-3)"> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_col12__66940a30 tabs_module_wrapper__05d921ca"> <div class="tabs_module_tabsViewport__05d921ca"> <div> <div data-rg-theme="purple" data-rg-mode="light" data-style-count="0"> <div class="tablist_module_tabList__515d0ebb"> <div role="presentation" aria-hidden="true" style="--tab-indicator-x:0px;--tab-indicator-width:0px" class="backgroundGradient_module_backgroundGradient__7e2959cb backgroundGradient_module_variantA__7e2959cb tablist_module_indicator__515d0ebb"></div> <ul role="tablist"> <li class="tablist_module_tab__515d0ebb" role="none presentation"><button role="tab" tabindex="0" aria-selected="true" id="tab-undefined-awt-content-topics#tab-text-media-how-generative-ai-evolved-1" aria-controls="tabpanel-undefined-awt-content-topics#tab-text-media-how-generative-ai-evolved-1" type="button" class="dropShadow_module_shadow__0e93b107 dropShadow_module_themed__0e93b107 dropShadow_module_onHover__0e93b107 dropShadow_module_pressable__0e93b107 dropShadow_module_disabled__0e93b107 buttonbase_module_buttonBase__efa86cab buttonbase_module_tertiary__efa86cab button_module_button__f561bac2"><span data-rg-n="UtilityText" class="basetext_module_text__61099e5e basetext_module_center__61099e5e utilitytext_module_utility__9ef276a8 button_module_content__f561bac2 utilitytext_module_size3__9ef276a8">VAEs</span></button></li> <li class="tablist_module_tab__515d0ebb" role="none presentation"><button role="tab" tabindex="-1" aria-selected="false" id="tab-undefined-awt-content-topics#tab-text-media-how-generative-ai-evolved-2" aria-controls="tabpanel-undefined-awt-content-topics#tab-text-media-how-generative-ai-evolved-2" type="button" class="dropShadow_module_shadow__0e93b107 dropShadow_module_themed__0e93b107 dropShadow_module_onHover__0e93b107 dropShadow_module_pressable__0e93b107 dropShadow_module_disabled__0e93b107 buttonbase_module_buttonBase__efa86cab buttonbase_module_tertiary__efa86cab button_module_button__f561bac2"><span data-rg-n="UtilityText" class="basetext_module_text__61099e5e basetext_module_center__61099e5e utilitytext_module_utility__9ef276a8 button_module_content__f561bac2 utilitytext_module_size3__9ef276a8">Transformers</span></button></li> <li class="tablist_module_tab__515d0ebb" role="none presentation"><button role="tab" tabindex="-1" aria-selected="false" id="tab-undefined-awt-content-topics#tab-text-media-how-generative-ai-evolved-3" aria-controls="tabpanel-undefined-awt-content-topics#tab-text-media-how-generative-ai-evolved-3" type="button" class="dropShadow_module_shadow__0e93b107 dropShadow_module_themed__0e93b107 dropShadow_module_onHover__0e93b107 dropShadow_module_pressable__0e93b107 dropShadow_module_disabled__0e93b107 buttonbase_module_buttonBase__efa86cab buttonbase_module_tertiary__efa86cab button_module_button__f561bac2"><span data-rg-n="UtilityText" class="basetext_module_text__61099e5e basetext_module_center__61099e5e utilitytext_module_utility__9ef276a8 button_module_content__f561bac2 utilitytext_module_size3__9ef276a8">The future</span></button></li> </ul> </div> </div> </div> </div> </div> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_col12__66940a30 tabs_module_panels__05d921ca"> <div class="tabs_module_scroller__05d921ca"> <div class="tabs_module_tabPanel__05d921ca tabs_module_active__05d921ca" role="tabpanel" id="tabpanel-undefined-awt-content-topics#tab-text-media-how-generative-ai-evolved-1" tabindex="0" aria-labelledby="tab-undefined-awt-content-topics#tab-text-media-how-generative-ai-evolved-1"> <div data-rg-n="TextMediaContent" data-media-position="end" class="textmediacontent_module_textMediaContent__d28b7a4a grid_module_grid__590284f4 grid_module_fullWidth__590284f4 grid_module_noPadding__590284f4" style="--rg-grid-gap:var(--rg-margin-3)"> <div data-rg-n="Col" data-text-form="long" class="col_module_col__66940a30 col_module_colXs12__66940a30 col_module_colS12__66940a30 col_module_colM6__66940a30 col_module_colL6__66940a30 col_module_colXl6__66940a30 col_module_colXxl6__66940a30 textmediacontent_module_textContainer__d28b7a4a"> <h3 data-rg-n="HeadingText" class="basetext_module_text__61099e5e headingtext_module_heading__00ee2f90 textmediacontent_module_heading__d28b7a4a headingtext_module_size3__00ee2f90">VAEs</h3> <div data-rg-n="BodyText" class="basetext_module_text__61099e5e bodytext_module_body__ca32720c bodytext_module_size2__ca32720c"> <p>VAEs (variational autoencoders) introduced the capability to create novel variations of multiple data types. This led to the rapid emergence of other generative AI models like generative adversarial networks and diffusion models. These innovations were focused on generating data that increasingly resembled real data despite being artificially created.</p> </div> </div> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_col1__66940a30 textmediacontent_module_spacer__d28b7a4a"></div> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_colXs12__66940a30 col_module_colS12__66940a30 col_module_colM5__66940a30 col_module_colL5__66940a30 col_module_colXl5__66940a30 col_module_colXxl5__66940a30 textmediacontent_module_mediaContainer__d28b7a4a"> <img class="image_module_image__0914b1c0" src="https://d1.awsstatic.com/Xilinix%20image.7256a7c3892be46cb7d2b29fa9358a33b2cdac40.png" alt="generative AI model" loading="lazy"> </div> </div> </div> <div class="tabs_module_tabPanel__05d921ca" role="tabpanel" id="tabpanel-undefined-awt-content-topics#tab-text-media-how-generative-ai-evolved-2" tabindex="-1" aria-labelledby="tab-undefined-awt-content-topics#tab-text-media-how-generative-ai-evolved-2"> <div data-rg-n="TextMediaContent" data-media-position="end" class="textmediacontent_module_textMediaContent__d28b7a4a grid_module_grid__590284f4 grid_module_fullWidth__590284f4 grid_module_noPadding__590284f4" style="--rg-grid-gap:var(--rg-margin-3)"> <div data-rg-n="Col" data-text-form="long" class="col_module_col__66940a30 col_module_colXs12__66940a30 col_module_colS12__66940a30 col_module_colM6__66940a30 col_module_colL6__66940a30 col_module_colXl6__66940a30 col_module_colXxl6__66940a30 textmediacontent_module_textContainer__d28b7a4a"> <h3 data-rg-n="HeadingText" class="basetext_module_text__61099e5e headingtext_module_heading__00ee2f90 textmediacontent_module_heading__d28b7a4a headingtext_module_size3__00ee2f90">Transformers</h3> <div data-rg-n="BodyText" class="basetext_module_text__61099e5e bodytext_module_body__ca32720c bodytext_module_size2__ca32720c"> <p>In 2017, a further shift in AI research occurred with the introduction of transformers. Transformers seamlessly integrated the encoder-and-decoder architecture with an attention mechanism. They streamlined the training process of language models with exceptional efficiency and versatility. Notable models like GPT emerged as foundational models capable of pretraining on extensive corpora of raw text and fine-tuning for diverse tasks.</p> <p>Transformers changed what was possible for natural language processing. They empowered generative capabilities for tasks ranging from translation and summarization to answering questions.</p> </div> </div> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_col1__66940a30 textmediacontent_module_spacer__d28b7a4a"></div> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_colXs12__66940a30 col_module_colS12__66940a30 col_module_colM5__66940a30 col_module_colL5__66940a30 col_module_colXl5__66940a30 col_module_colXxl5__66940a30 textmediacontent_module_mediaContainer__d28b7a4a"> <img class="image_module_image__0914b1c0" src="https://d1.awsstatic.com/HOME_Next_Marketplace.a50fc6d00cab4ac0127dc73e10ff1e53737a8423.jpg" alt="generative AI blocks" loading="lazy"> </div> </div> </div> <div class="tabs_module_tabPanel__05d921ca" role="tabpanel" id="tabpanel-undefined-awt-content-topics#tab-text-media-how-generative-ai-evolved-3" tabindex="-1" aria-labelledby="tab-undefined-awt-content-topics#tab-text-media-how-generative-ai-evolved-3"> <div data-rg-n="TextMediaContent" data-media-position="end" class="textmediacontent_module_textMediaContent__d28b7a4a grid_module_grid__590284f4 grid_module_fullWidth__590284f4 grid_module_noPadding__590284f4" style="--rg-grid-gap:var(--rg-margin-3)"> <div data-rg-n="Col" data-text-form="long" class="col_module_col__66940a30 col_module_colXs12__66940a30 col_module_colS12__66940a30 col_module_colM6__66940a30 col_module_colL6__66940a30 col_module_colXl6__66940a30 col_module_colXxl6__66940a30 textmediacontent_module_textContainer__d28b7a4a"> <h3 data-rg-n="HeadingText" class="basetext_module_text__61099e5e headingtext_module_heading__00ee2f90 textmediacontent_module_heading__d28b7a4a headingtext_module_size3__00ee2f90">The future</h3> <div data-rg-n="BodyText" class="basetext_module_text__61099e5e bodytext_module_body__ca32720c bodytext_module_size2__ca32720c"> <p>Many generative AI models continue to make significant strides and have found cross-industry applications. Recent innovations focus on refining models to work with proprietary data. Researchers also want to create text, images, videos, and speech that are more and more human-like.</p> </div> </div> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_col1__66940a30 textmediacontent_module_spacer__d28b7a4a"></div> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_colXs12__66940a30 col_module_colS12__66940a30 col_module_colM5__66940a30 col_module_colL5__66940a30 col_module_colXl5__66940a30 col_module_colXxl5__66940a30 textmediacontent_module_mediaContainer__d28b7a4a"> <img class="image_module_image__0914b1c0" src="https://d1.awsstatic.com/isv/migrate-modernize-step-thumbnail-3D-image-of-several-linear-layers-ornage-to-pink-gradient-4x3%402x.3db1a23cef4b302f912558150e40856a00f0608a.jpg" alt="generative AI future" loading="lazy"> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div></span> </div> </div> </div> <div class="lb-grid lb-row lb-row-max-large lb-snap"> <div class="lb-col lb-tiny-24 lb-mid-24"> <h2 id="How_does_generative_AI_work.3F" class="lb-txt-none lb-txt-40 lb-h2 lb-title"> How does generative AI work?</h2> <div class="lb-txt-17 lb-rtxt"> <p>Like all artificial intelligence, generative AI works by using machine learning models—very large models that are pre-trained on vast amounts of data.</p> <p><b>Foundation models</b></p> <p><a href="/what-is/foundation-models/">Foundation models</a> (FMs) are ML models trained on a broad spectrum of generalized and unlabeled data. They are capable of performing a wide variety of general tasks.</p> <p>FMs are the result of the latest advancements in a technology that has been evolving for decades. In general, an FM uses learned patterns and relationships to predict the next item in a sequence.</p> <p>For example, with image generation, the model analyzes the image and creates a sharper, more clearly defined version of the image. Similarly, with text, the model predicts the next word in a string of text based on the previous words and their context. It then selects the next word using probability distribution techniques.</p> <p><b>Large language models</b></p> <p><a href="/what-is/large-language-model/">Large language models</a> (LLMs) are one class of FMs. For example, OpenAI's generative pre-trained transformer (GPT) models are LLMs. LLMs are specifically focused on language-based tasks such as such as summarization, text generation, classification, open-ended conversation, and information extraction.</p> <p><a href="/what-is/gpt/">Read about GPT »</a></p> <p>What makes LLMs special is their ability to perform multiple tasks. They can do this because they contain many parameters that make them capable of learning advanced concepts.</p> <p>An LLM like GPT-3 can consider billions of parameters and has the ability to generate content from very little input. Through their pretraining exposure to internet-scale data in all its various forms and myriad patterns, LLMs learn to apply their knowledge in a wide range of contexts.</p> </div> </div> </div> <div data-eb-slot="tab-text-media-gen-ai-models-work" data-eb-slot-meta="{'version':'1.0','slotId':'tab-text-media-gen-ai-models-work','experienceId':'a7569809-5370-4110-ba5b-b74892c435d9','allowBlank':false,'hasAltExp':false,'isRTR':false,'filters':{'limit':7,'query':'(id \u003d \'tab-text-media-how-do-generative-ai-models-work-header\' OR id \u003d \'tab-text-media-how-do-generative-ai-models-work-1\' OR id \u003d \'tab-text-media-how-do-generative-ai-models-work-2\' OR id \u003d \'tab-text-media-how-do-generative-ai-models-work-3\' OR id \u003d \'tab-text-media-how-do-generative-ai-models-work-4\')'}}"> <div data-eb-tpl-n="awsm-rt/rt-text-media-tabs" data-eb-tpl-v="1.0.0" data-eb-ce="" data-eb-c-scope="tab-text-media-gen-ai-models-work" data-eb-d-scope="DIRECTORIES" data-eb-locale="en-US" data-eb-369baad2="" data-eb-ssr-ce="" data-eb-tpl-ns="awsmRT" data-eb-hydrated="pending"> <script type="application/json">{"data":{"items":[{"fields":{"patternHeading":"How do generative AI models work?","id":"awt-content-topics#tab-text-media-how-do-generative-ai-models-work-header","itemCustomSortOrder":"0","patternSubheading":"Traditional machine learning models were discriminative or focused on classifying data points. They attempted to determine the relationship between known and unknown factors. For example, they look at images—known data like pixel arrangement, line, color, and shape—and map them to words—the unknown factor. Mathematically, the models worked by identifying equations that could numerically map unknown and known factors as x and y variables. Generative models take this one step further. Instead of predicting a label given some features, they try to predict features given a certain label. Mathematically, generative modeling calculates the probability of x and y occurring together. It learns the distribution of different data features and their relationships. For example, generative models analyze animal images to record variables like different ear shapes, eye shapes, tail features, and skin patterns. They learn features and their relations to understand what different animals look like in general. They can then recreate new animal images that were not in the training set. Next, we give some broad categories of generative AI models."},"metadata":{"tags":[{"id":"GLOBAL#pattern#pattern-data","name":"pattern-data","namespaceId":"GLOBAL#pattern","description":"pattern-data","metadata":{}}]}},{"fields":{"patternBoolean2":"0","patternBoolean3":"0","itemCustomSortOrder":"1","itemLongLoc":"<p>Diffusion models create new data by iteratively making controlled random changes to an initial data sample. They start with the original data and add subtle changes (noise), progressively making it less similar to the original. This noise is carefully controlled to ensure the generated data remains coherent and realistic.</p> \n<p>After adding noise over several iterations, the diffusion model reverses the process. Reverse denoising gradually removes the noise to produce a new data sample that resembles the original.</p>","itemMediaAltText":"Diffusion models","itemOption":"right","itemMediaURL":"https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2023/05/24/stable-diffusion-1.jpg","itemHeading":"Diffusion models","id":"awt-content-topics#tab-text-media-how-do-generative-ai-models-work-1","itemTextLoc3":"Diffusion models","itemBoolean":"0","patternBoolean":"0"},"metadata":{"tags":[]}},{"fields":{"patternBoolean2":"0","patternBoolean3":"0","itemCustomSortOrder":"2","itemLongLoc":"<p>The <a href=\"https://aws.amazon.com/what-is/gan/\" style=\"color:#0563c1; text-decoration:underline\" title=\"https://aws.amazon.com/what-is/gan/\">generative adversarial network</a> (GAN) is another generative AI model that builds upon the diffusion model’s concept.</p> \n<p>GANs work by training two neural networks in a competitive manner. The first network, known as the generator, generates fake data samples by adding random noise. The second network called the discriminator, tries to distinguish between real data and the fake data produced by the generator. </p> \n<p>During training, the generator continually improves its ability to create realistic data while the discriminator becomes better at telling real from fake. This adversarial process continues until the generator produces data that is so convincing that the discriminator can't differentiate it from real data.</p> \n<p>GANs are widely used in generating realistic images, style transfer, and data augmentation tasks.</p>","itemMediaAltText":"Generative adversarial networks","itemOption":"right","itemMediaURL":"https://d1.awsstatic.com/products/generative-ai/Genai_main_P2genaitoolseverybiz_square_large.ebe310a2802c5c65a0dd7ab86561b3afa862e4d2.jpg","itemHeading":"Generative adversarial networks","id":"awt-content-topics#tab-text-media-how-do-generative-ai-models-work-2","itemTextLoc3":"Generative adversarial networks","itemBoolean":"0","patternBoolean":"0"},"metadata":{"tags":[]}},{"fields":{"patternBoolean2":"0","patternBoolean3":"0","itemCustomSortOrder":"3","itemLongLoc":"<p>Variational autoencoders (VAEs) learn a compact representation of data called latent space. The latent space is a mathematical representation of the data. You can think of it as a unique code representing the data based on all its attributes. For example, if studying faces, the latent space contains numbers representing eye shape, nose shape, cheekbones, and ears.</p> \n<p>VAEs use two neural networks—the encoder and the decoder. The encoder neural network maps the input data to a mean and variance for each dimension of the latent space. It generates a random sample from a Gaussian (normal) distribution. This sample is a point in the latent space and represents a compressed, simplified version of the input data.</p> \n<p>The decoder neural network takes this sampled point from the latent space and reconstructs it back into data that resembles the original input. Mathematical functions are used to measure how well the reconstructed data matches the original data.</p>","itemMediaAltText":"Variational autoencoders","itemOption":"right","itemMediaURL":"https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2023/02/22/clipseg_architecture-1.png","itemHeading":"Variational autoencoders","id":"awt-content-topics#tab-text-media-how-do-generative-ai-models-work-3","itemTextLoc3":"Variational autoencoders","itemBoolean":"0","patternBoolean":"0"},"metadata":{"tags":[]}},{"fields":{"patternBoolean2":"0","patternBoolean3":"0","itemCustomSortOrder":"4","itemLongLoc":"<p>The transformer-based generative AI model builds upon the encoder and decoder concepts of VAEs. Transformer-based models add more layers to the encoder to improve performance on text-based tasks like comprehension, translation, and creative writing.</p> \n<p>Transformer-based models use a self-attention mechanism. They weigh the importance of different parts of an input sequence when processing each element in the sequence.</p> \n<p>Another key feature is that these AI models implement contextual embeddings. The encoding of a sequence element depends not only on the element itself but also on its context within the sequence.</p> \n<h4>How transformer-based models work</h4> \n<p>To understand how transformer-based models work, imagine a sentence as a sequence of words.</p> \n<p>Self-attention helps the model focus on the relevant words as it processes each word. The transformer-based generative model employs multiple encoder layers called attention heads to capture different types of relationships between words. Each head learns to attend to different parts of the input sequence, allowing the model to simultaneously consider various aspects of the data.</p> \n<p>Each layer also refines the contextual embeddings, making them more informative and capturing everything from grammar syntax to complex semantic meanings.</p>","itemMediaAltText":"Transformer-based models","itemOption":"right","itemMediaURL":"https://d2908q01vomqb2.cloudfront.net/a17554a0d2b15a664c0e73900184544f19e70227/2024/09/23/3D-Model-1-300x245.png","itemHeading":"Transformer-based models","id":"awt-content-topics#tab-text-media-how-do-generative-ai-models-work-4","itemTextLoc3":"Transformer-based models","itemBoolean":"0","patternBoolean":"0"},"metadata":{"tags":[]}}]},"metadata":{"auth":{},"pagination":{"empty":false,"present":true},"testAttributes":{}},"context":{"page":{"pageUrl":"https://aws.amazon.com/what-is/generative-ai/"},"environment":{"stage":"prod","region":"us-east-1"},"sdkVersion":"1.0.129"},"refMap":{"manifest.js":"a5170d6503","rt-text-media-tabs.rtl.css":"0314c48d51","rt-text-media-tabs.css":"60d47b7033","rt-text-media-tabs.js":"ca7b533f5a","rt-text-media-tabs.css.js":"fd098b3612","rt-text-media-tabs.rtl.css.js":"081a4ddd66"},"settings":{"templateMappings":{"tabLabel":"itemTextLoc3","patternHeading":"patternHeading","patternSubheading":"patternSubheading","patternTheme":"patternTextNoLoc","hyperlinkText":"itemCTALabel","hyperlinkUrl":"itemCTAURL","heading":"itemHeading","dark":"itemBoolean","mediaAltText":"itemMediaAltText","mediaPosition":"itemOption","mediaUrl":"itemMediaURL","body":"itemLongLoc","videoThumbnailUrl":"itemMediaURL2","videoOverlayTitle":"itemMediaAltText2","videoPlayButtonText":"itemTextLoc2"}}}</script> <div data-eb-tpl-root="" data-reactroot=""> <span data-eb-item-id="awt-content-topics#tab-text-media-how-do-generative-ai-models-work-header"> <div data-rg-lang="en" data-rg-locale="en-US" lang="en-US" dir="ltr"> <div data-rg-theme="fuchsia" data-rg-mode="light" data-style-count="0"> <div data-rg-n="GridStack" class="templatecontainer_module_templateContainer__dd3d201a gridStack_module_gridStack__35667094"> <div data-rg-n="Grid" class="grid_module_grid__590284f4" style="--rg-grid-gap:var(--rg-margin-3)"> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_col12__66940a30"> <div class="templateHeader_module_templateHeader__b496fe91"> <div data-rg-n="Grid" class="grid_module_grid__590284f4 grid_module_noPadding__590284f4" style="--rg-grid-gap:var(--rg-margin-3)"> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_colXs12__66940a30 col_module_colS10__66940a30 col_module_colM8__66940a30 col_module_colL8__66940a30 col_module_colXl8__66940a30 col_module_colXxl8__66940a30"> <h2 data-rg-n="HeadingText" class="basetext_module_text__61099e5e headingtext_module_heading__00ee2f90 headingtext_module_size2__00ee2f90">How do generative AI models work?</h2> <p data-rg-n="SubheadingText" class="basetext_module_text__61099e5e subheadingtext_module_subheading__99e66897 subheadingtext_module_size2__99e66897">Traditional machine learning models were discriminative or focused on classifying data points. They attempted to determine the relationship between known and unknown factors. For example, they look at images—known data like pixel arrangement, line, color, and shape—and map them to words—the unknown factor. Mathematically, the models worked by identifying equations that could numerically map unknown and known factors as x and y variables. Generative models take this one step further. Instead of predicting a label given some features, they try to predict features given a certain label. Mathematically, generative modeling calculates the probability of x and y occurring together. It learns the distribution of different data features and their relationships. For example, generative models analyze animal images to record variables like different ear shapes, eye shapes, tail features, and skin patterns. They learn features and their relations to understand what different animals look like in general. They can then recreate new animal images that were not in the training set. Next, we give some broad categories of generative AI models.</p> </div> </div> </div> </div> </div> <div data-rg-n="Grid" class="grid_module_grid__590284f4" style="--rg-grid-gap:var(--rg-margin-3)"> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_col12__66940a30"> <div data-rg-n="Grid" class="tabs_module_tabs__05d921ca grid_module_grid__590284f4 grid_module_noPadding__590284f4" style="--panel-width:100%;--scroller-offset:0;--rg-grid-gap:var(--rg-margin-3)"> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_col12__66940a30 tabs_module_wrapper__05d921ca"> <div class="tabs_module_tabsViewport__05d921ca"> <div> <div data-rg-theme="fuchsia" data-rg-mode="light" data-style-count="0"> <div class="tablist_module_tabList__515d0ebb"> <div role="presentation" aria-hidden="true" style="--tab-indicator-x:0px;--tab-indicator-width:0px" class="backgroundGradient_module_backgroundGradient__7e2959cb backgroundGradient_module_variantA__7e2959cb tablist_module_indicator__515d0ebb"></div> <ul role="tablist"> <li class="tablist_module_tab__515d0ebb" role="none presentation"><button role="tab" tabindex="0" aria-selected="true" id="tab-undefined-awt-content-topics#tab-text-media-how-do-generative-ai-models-work-1" aria-controls="tabpanel-undefined-awt-content-topics#tab-text-media-how-do-generative-ai-models-work-1" type="button" class="dropShadow_module_shadow__0e93b107 dropShadow_module_themed__0e93b107 dropShadow_module_onHover__0e93b107 dropShadow_module_pressable__0e93b107 dropShadow_module_disabled__0e93b107 buttonbase_module_buttonBase__efa86cab buttonbase_module_tertiary__efa86cab button_module_button__f561bac2"><span data-rg-n="UtilityText" class="basetext_module_text__61099e5e basetext_module_center__61099e5e utilitytext_module_utility__9ef276a8 button_module_content__f561bac2 utilitytext_module_size3__9ef276a8">Diffusion models</span></button></li> <li class="tablist_module_tab__515d0ebb" role="none presentation"><button role="tab" tabindex="-1" aria-selected="false" id="tab-undefined-awt-content-topics#tab-text-media-how-do-generative-ai-models-work-2" aria-controls="tabpanel-undefined-awt-content-topics#tab-text-media-how-do-generative-ai-models-work-2" type="button" class="dropShadow_module_shadow__0e93b107 dropShadow_module_themed__0e93b107 dropShadow_module_onHover__0e93b107 dropShadow_module_pressable__0e93b107 dropShadow_module_disabled__0e93b107 buttonbase_module_buttonBase__efa86cab buttonbase_module_tertiary__efa86cab button_module_button__f561bac2"><span data-rg-n="UtilityText" class="basetext_module_text__61099e5e basetext_module_center__61099e5e utilitytext_module_utility__9ef276a8 button_module_content__f561bac2 utilitytext_module_size3__9ef276a8">Generative adversarial networks</span></button></li> <li class="tablist_module_tab__515d0ebb" role="none presentation"><button role="tab" tabindex="-1" aria-selected="false" id="tab-undefined-awt-content-topics#tab-text-media-how-do-generative-ai-models-work-3" aria-controls="tabpanel-undefined-awt-content-topics#tab-text-media-how-do-generative-ai-models-work-3" type="button" class="dropShadow_module_shadow__0e93b107 dropShadow_module_themed__0e93b107 dropShadow_module_onHover__0e93b107 dropShadow_module_pressable__0e93b107 dropShadow_module_disabled__0e93b107 buttonbase_module_buttonBase__efa86cab buttonbase_module_tertiary__efa86cab button_module_button__f561bac2"><span data-rg-n="UtilityText" class="basetext_module_text__61099e5e basetext_module_center__61099e5e utilitytext_module_utility__9ef276a8 button_module_content__f561bac2 utilitytext_module_size3__9ef276a8">Variational autoencoders</span></button></li> <li class="tablist_module_tab__515d0ebb" role="none presentation"><button role="tab" tabindex="-1" aria-selected="false" id="tab-undefined-awt-content-topics#tab-text-media-how-do-generative-ai-models-work-4" aria-controls="tabpanel-undefined-awt-content-topics#tab-text-media-how-do-generative-ai-models-work-4" type="button" class="dropShadow_module_shadow__0e93b107 dropShadow_module_themed__0e93b107 dropShadow_module_onHover__0e93b107 dropShadow_module_pressable__0e93b107 dropShadow_module_disabled__0e93b107 buttonbase_module_buttonBase__efa86cab buttonbase_module_tertiary__efa86cab button_module_button__f561bac2"><span data-rg-n="UtilityText" class="basetext_module_text__61099e5e basetext_module_center__61099e5e utilitytext_module_utility__9ef276a8 button_module_content__f561bac2 utilitytext_module_size3__9ef276a8">Transformer-based models</span></button></li> </ul> </div> </div> </div> </div> </div> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_col12__66940a30 tabs_module_panels__05d921ca"> <div class="tabs_module_scroller__05d921ca"> <div class="tabs_module_tabPanel__05d921ca tabs_module_active__05d921ca" role="tabpanel" id="tabpanel-undefined-awt-content-topics#tab-text-media-how-do-generative-ai-models-work-1" tabindex="0" aria-labelledby="tab-undefined-awt-content-topics#tab-text-media-how-do-generative-ai-models-work-1"> <div data-rg-n="TextMediaContent" data-media-position="end" class="textmediacontent_module_textMediaContent__d28b7a4a grid_module_grid__590284f4 grid_module_fullWidth__590284f4 grid_module_noPadding__590284f4" style="--rg-grid-gap:var(--rg-margin-3)"> <div data-rg-n="Col" data-text-form="long" class="col_module_col__66940a30 col_module_colXs12__66940a30 col_module_colS12__66940a30 col_module_colM6__66940a30 col_module_colL6__66940a30 col_module_colXl6__66940a30 col_module_colXxl6__66940a30 textmediacontent_module_textContainer__d28b7a4a"> <h3 data-rg-n="HeadingText" class="basetext_module_text__61099e5e headingtext_module_heading__00ee2f90 textmediacontent_module_heading__d28b7a4a headingtext_module_size3__00ee2f90">Diffusion models</h3> <div data-rg-n="BodyText" class="basetext_module_text__61099e5e bodytext_module_body__ca32720c bodytext_module_size2__ca32720c"> <p>Diffusion models create new data by iteratively making controlled random changes to an initial data sample. They start with the original data and add subtle changes (noise), progressively making it less similar to the original. This noise is carefully controlled to ensure the generated data remains coherent and realistic.</p> <p>After adding noise over several iterations, the diffusion model reverses the process. Reverse denoising gradually removes the noise to produce a new data sample that resembles the original.</p> </div> </div> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_col1__66940a30 textmediacontent_module_spacer__d28b7a4a"></div> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_colXs12__66940a30 col_module_colS12__66940a30 col_module_colM5__66940a30 col_module_colL5__66940a30 col_module_colXl5__66940a30 col_module_colXxl5__66940a30 textmediacontent_module_mediaContainer__d28b7a4a"> <img class="image_module_image__0914b1c0" src="https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2023/05/24/stable-diffusion-1.jpg" alt="Diffusion models" loading="lazy"> </div> </div> </div> <div class="tabs_module_tabPanel__05d921ca" role="tabpanel" id="tabpanel-undefined-awt-content-topics#tab-text-media-how-do-generative-ai-models-work-2" tabindex="-1" aria-labelledby="tab-undefined-awt-content-topics#tab-text-media-how-do-generative-ai-models-work-2"> <div data-rg-n="TextMediaContent" data-media-position="end" class="textmediacontent_module_textMediaContent__d28b7a4a grid_module_grid__590284f4 grid_module_fullWidth__590284f4 grid_module_noPadding__590284f4" style="--rg-grid-gap:var(--rg-margin-3)"> <div data-rg-n="Col" data-text-form="long" class="col_module_col__66940a30 col_module_colXs12__66940a30 col_module_colS12__66940a30 col_module_colM6__66940a30 col_module_colL6__66940a30 col_module_colXl6__66940a30 col_module_colXxl6__66940a30 textmediacontent_module_textContainer__d28b7a4a"> <h3 data-rg-n="HeadingText" class="basetext_module_text__61099e5e headingtext_module_heading__00ee2f90 textmediacontent_module_heading__d28b7a4a headingtext_module_size3__00ee2f90">Generative adversarial networks</h3> <div data-rg-n="BodyText" class="basetext_module_text__61099e5e bodytext_module_body__ca32720c bodytext_module_size2__ca32720c"> <p>The <a href="https://aws.amazon.com/what-is/gan/">generative adversarial network</a> (GAN) is another generative AI model that builds upon the diffusion model’s concept.</p> <p>GANs work by training two neural networks in a competitive manner. The first network, known as the generator, generates fake data samples by adding random noise. The second network called the discriminator, tries to distinguish between real data and the fake data produced by the generator. </p> <p>During training, the generator continually improves its ability to create realistic data while the discriminator becomes better at telling real from fake. This adversarial process continues until the generator produces data that is so convincing that the discriminator can't differentiate it from real data.</p> <p>GANs are widely used in generating realistic images, style transfer, and data augmentation tasks.</p> </div> </div> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_col1__66940a30 textmediacontent_module_spacer__d28b7a4a"></div> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_colXs12__66940a30 col_module_colS12__66940a30 col_module_colM5__66940a30 col_module_colL5__66940a30 col_module_colXl5__66940a30 col_module_colXxl5__66940a30 textmediacontent_module_mediaContainer__d28b7a4a"> <img class="image_module_image__0914b1c0" src="https://d1.awsstatic.com/products/generative-ai/Genai_main_P2genaitoolseverybiz_square_large.ebe310a2802c5c65a0dd7ab86561b3afa862e4d2.jpg" alt="Generative adversarial networks" loading="lazy"> </div> </div> </div> <div class="tabs_module_tabPanel__05d921ca" role="tabpanel" id="tabpanel-undefined-awt-content-topics#tab-text-media-how-do-generative-ai-models-work-3" tabindex="-1" aria-labelledby="tab-undefined-awt-content-topics#tab-text-media-how-do-generative-ai-models-work-3"> <div data-rg-n="TextMediaContent" data-media-position="end" class="textmediacontent_module_textMediaContent__d28b7a4a grid_module_grid__590284f4 grid_module_fullWidth__590284f4 grid_module_noPadding__590284f4" style="--rg-grid-gap:var(--rg-margin-3)"> <div data-rg-n="Col" data-text-form="long" class="col_module_col__66940a30 col_module_colXs12__66940a30 col_module_colS12__66940a30 col_module_colM6__66940a30 col_module_colL6__66940a30 col_module_colXl6__66940a30 col_module_colXxl6__66940a30 textmediacontent_module_textContainer__d28b7a4a"> <h3 data-rg-n="HeadingText" class="basetext_module_text__61099e5e headingtext_module_heading__00ee2f90 textmediacontent_module_heading__d28b7a4a headingtext_module_size3__00ee2f90">Variational autoencoders</h3> <div data-rg-n="BodyText" class="basetext_module_text__61099e5e bodytext_module_body__ca32720c bodytext_module_size2__ca32720c"> <p>Variational autoencoders (VAEs) learn a compact representation of data called latent space. The latent space is a mathematical representation of the data. You can think of it as a unique code representing the data based on all its attributes. For example, if studying faces, the latent space contains numbers representing eye shape, nose shape, cheekbones, and ears.</p> <p>VAEs use two neural networks—the encoder and the decoder. The encoder neural network maps the input data to a mean and variance for each dimension of the latent space. It generates a random sample from a Gaussian (normal) distribution. This sample is a point in the latent space and represents a compressed, simplified version of the input data.</p> <p>The decoder neural network takes this sampled point from the latent space and reconstructs it back into data that resembles the original input. Mathematical functions are used to measure how well the reconstructed data matches the original data.</p> </div> </div> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_col1__66940a30 textmediacontent_module_spacer__d28b7a4a"></div> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_colXs12__66940a30 col_module_colS12__66940a30 col_module_colM5__66940a30 col_module_colL5__66940a30 col_module_colXl5__66940a30 col_module_colXxl5__66940a30 textmediacontent_module_mediaContainer__d28b7a4a"> <img class="image_module_image__0914b1c0" src="https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2023/02/22/clipseg_architecture-1.png" alt="Variational autoencoders" loading="lazy"> </div> </div> </div> <div class="tabs_module_tabPanel__05d921ca" role="tabpanel" id="tabpanel-undefined-awt-content-topics#tab-text-media-how-do-generative-ai-models-work-4" tabindex="-1" aria-labelledby="tab-undefined-awt-content-topics#tab-text-media-how-do-generative-ai-models-work-4"> <div data-rg-n="TextMediaContent" data-media-position="end" class="textmediacontent_module_textMediaContent__d28b7a4a grid_module_grid__590284f4 grid_module_fullWidth__590284f4 grid_module_noPadding__590284f4" style="--rg-grid-gap:var(--rg-margin-3)"> <div data-rg-n="Col" data-text-form="long" class="col_module_col__66940a30 col_module_colXs12__66940a30 col_module_colS12__66940a30 col_module_colM6__66940a30 col_module_colL6__66940a30 col_module_colXl6__66940a30 col_module_colXxl6__66940a30 textmediacontent_module_textContainer__d28b7a4a"> <h3 data-rg-n="HeadingText" class="basetext_module_text__61099e5e headingtext_module_heading__00ee2f90 textmediacontent_module_heading__d28b7a4a headingtext_module_size3__00ee2f90">Transformer-based models</h3> <div data-rg-n="BodyText" class="basetext_module_text__61099e5e bodytext_module_body__ca32720c bodytext_module_size2__ca32720c"> <p>The transformer-based generative AI model builds upon the encoder and decoder concepts of VAEs. Transformer-based models add more layers to the encoder to improve performance on text-based tasks like comprehension, translation, and creative writing.</p> <p>Transformer-based models use a self-attention mechanism. They weigh the importance of different parts of an input sequence when processing each element in the sequence.</p> <p>Another key feature is that these AI models implement contextual embeddings. The encoding of a sequence element depends not only on the element itself but also on its context within the sequence.</p> <h4>How transformer-based models work</h4> <p>To understand how transformer-based models work, imagine a sentence as a sequence of words.</p> <p>Self-attention helps the model focus on the relevant words as it processes each word. The transformer-based generative model employs multiple encoder layers called attention heads to capture different types of relationships between words. Each head learns to attend to different parts of the input sequence, allowing the model to simultaneously consider various aspects of the data.</p> <p>Each layer also refines the contextual embeddings, making them more informative and capturing everything from grammar syntax to complex semantic meanings.</p> </div> </div> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_col1__66940a30 textmediacontent_module_spacer__d28b7a4a"></div> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_colXs12__66940a30 col_module_colS12__66940a30 col_module_colM5__66940a30 col_module_colL5__66940a30 col_module_colXl5__66940a30 col_module_colXxl5__66940a30 textmediacontent_module_mediaContainer__d28b7a4a"> <img class="image_module_image__0914b1c0" src="https://d2908q01vomqb2.cloudfront.net/a17554a0d2b15a664c0e73900184544f19e70227/2024/09/23/3D-Model-1-300x245.png" alt="Transformer-based models" loading="lazy"> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div></span> </div> </div> </div> <div data-eb-slot="generative-ai-training-beginners-overview" data-eb-slot-meta="{'version':'1.0','slotId':'generative-ai-training-beginners-overview','experienceId':'49b9c725-36e2-4908-86d9-b64cc9f56589','allowBlank':true,'hasAltExp':false,'isRTR':false,'filters':{'limit':1,'query':'id \u003d \'generative-ai-training-beginners-overview\''}}"> <div data-eb-tpl-n="awsm-rt/rt-text-media-collection" data-eb-tpl-v="1.2.0" data-eb-ce="" data-eb-c-scope="generative-ai-training-beginners-overview" data-eb-d-scope="DIRECTORIES" data-eb-locale="en-US" data-eb-7170ec75="" data-eb-ssr-ce="" data-eb-tpl-ns="awsmRT" data-eb-hydrated="pending"> <script type="application/json">{"data":{"items":[{"fields":{"mediaUrl":"https://d1.awsstatic.com/industry/telecom/2023-site/reimagining-consumer-experience.8676baa87a7f0fc3292ebf1db39602366fd42c1c.jpg","heading":"Generative AI training for beginners","dark":"0","bodyContent":"<p>Generative AI training begins with understanding foundational machine learning concepts. Learners also have to explore neural networks and AI architecture. Practical experience with Python libraries such as TensorFlow or PyTorch is essential for implementing and experimenting with different models. You also have to learn model evaluation, fine tuning and prompt engineering skills.</p> \n<p>A degree in artificial intelligence or machine learning provides in-depth training. Consider online short courses and certifications for professional development. <a href=\"https://aws.amazon.com/training/learn-about/generative-ai/\" style=\"color:#0563c1; text-decoration:underline\" title=\"https://aws.amazon.com/training/learn-about/generative-ai/\">Generative AI training on AWS</a> includes certifications by AWS experts on topics like:</p> \n<ul> \n <li><a href=\"https://explore.skillbuilder.aws/learn/course/external/view/elearning/17176/introduction-to-generative-ai-art-of-the-possible\" style=\"color:#0563c1; text-decoration:underline\" title=\"https://explore.skillbuilder.aws/learn/course/external/view/elearning/17176/introduction-to-generative-ai-art-of-the-possible\">Introduction to generative AI</a></li> \n <li><a href=\"https://explore.skillbuilder.aws/learn/course/external/view/elearning/16666/generative-ai-for-executives\" style=\"color:#0563c1; text-decoration:underline\" title=\"https://explore.skillbuilder.aws/learn/course/external/view/elearning/16666/generative-ai-for-executives\">Generative AI for executives</a></li> \n <li><a href=\"https://training.resources.awscloud.com/partner-ai-ml-training/aws-partner-generative-ai-essentials-business\" style=\"color:#0563c1; text-decoration:underline\" title=\"https://training.resources.awscloud.com/partner-ai-ml-training/aws-partner-generative-ai-essentials-business\">Generative AI essentials for business</a></li> \n</ul> \n<p> </p>","id":"collection-text-media#generative-ai-training-beginners-overview","mediaPosition":"left","mediaAltText":"Generative AI training for beginners"},"metadata":{"tags":[]}}]},"metadata":{"auth":{},"testAttributes":{}},"context":{"page":{"pageUrl":"https://aws.amazon.com/what-is/generative-ai/"},"environment":{"stage":"prod","region":"us-east-1"},"sdkVersion":"1.0.129"},"refMap":{"manifest.js":"47864e0370","rt-text-media-collection.rtl.css":"4a56177a31","rt-text-media-collection.js":"549552b6f3","rt-text-media-collection.css.js":"b7f4f2a16e","rt-text-media-collection.css":"dbf6370bb5","rt-text-media-collection.rtl.css.js":"641e7324ee"},"settings":{"templateMappings":{"hyperlinkText":"hyperlinkText","hyperlinkUrl":"hyperlinkUrl","heading":"heading","mediaAltText":"mediaAltText","mediaPosition":"mediaPosition","mediaUrl":"mediaUrl","subheader":"subheader","bodyContent":"bodyContent","videoOverlayTitle":"videoOverlayTitle","videoThumbnailUrl":"videoThumbnailUrl","videoPlayButtonText":"videoPlayButtonText","dark":"dark"}}}</script> <div data-eb-tpl-root="" data-reactroot=""> <div> <div data-rg-theme="blue" data-rg-mode="light" data-style-count="0"> <div data-rg-lang="en" data-rg-locale="en-US" lang="en-US" dir="ltr"> <div data-rg-n="GridStack" class="gridStack_module_gridStack__35667094" style="--rg-gridstack-gap:var(--rg-margin-2)"> <div data-rg-n="TextMediaContent" data-rg-theme="orange" data-rg-mode="light" data-media-position="start" class="textmediacontent_module_textMediaContent__d28b7a4a textmediacontent_module_gridPadding__d28b7a4a grid_module_grid__590284f4 grid_module_fullWidth__590284f4 grid_module_vPadding__590284f4" style="--rg-grid-gap:var(--rg-margin-3)"> <div data-rg-n="Col" data-text-form="long" class="col_module_col__66940a30 col_module_colXs12__66940a30 col_module_colS12__66940a30 col_module_colM6__66940a30 col_module_colL6__66940a30 col_module_colXl6__66940a30 col_module_colXxl6__66940a30 textmediacontent_module_textContainer__d28b7a4a"> <h2 data-rg-n="HeadingText" class="basetext_module_text__61099e5e headingtext_module_heading__00ee2f90 textmediacontent_module_heading__d28b7a4a headingtext_module_size2__00ee2f90">Generative AI training for beginners</h2> <div data-rg-n="BodyText" class="basetext_module_text__61099e5e bodytext_module_body__ca32720c bodytext_module_size1__ca32720c"> <p>Generative AI training begins with understanding foundational machine learning concepts. Learners also have to explore neural networks and AI architecture. Practical experience with Python libraries such as TensorFlow or PyTorch is essential for implementing and experimenting with different models. You also have to learn model evaluation, fine tuning and prompt engineering skills.</p> <p>A degree in artificial intelligence or machine learning provides in-depth training. Consider online short courses and certifications for professional development. <a href="https://aws.amazon.com/training/learn-about/generative-ai/">Generative AI training on AWS</a> includes certifications by AWS experts on topics like:</p> <ul> <li><a href="https://explore.skillbuilder.aws/learn/course/external/view/elearning/17176/introduction-to-generative-ai-art-of-the-possible">Introduction to generative AI</a></li> <li><a href="https://explore.skillbuilder.aws/learn/course/external/view/elearning/16666/generative-ai-for-executives">Generative AI for executives</a></li> <li><a href="https://training.resources.awscloud.com/partner-ai-ml-training/aws-partner-generative-ai-essentials-business">Generative AI essentials for business</a></li> </ul> <p> </p> </div> </div> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_col1__66940a30 textmediacontent_module_spacer__d28b7a4a"></div> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_colXs12__66940a30 col_module_colS12__66940a30 col_module_colM5__66940a30 col_module_colL5__66940a30 col_module_colXl5__66940a30 col_module_colXxl5__66940a30 textmediacontent_module_mediaContainer__d28b7a4a"> <img class="image_module_image__0914b1c0" src="https://d1.awsstatic.com/industry/telecom/2023-site/reimagining-consumer-experience.8676baa87a7f0fc3292ebf1db39602366fd42c1c.jpg" alt="Generative AI training for beginners" loading="lazy"> </div> </div> </div> </div> </div> </div> </div> </div> </div> <div class="lb-grid lb-row lb-row-max-large lb-snap"> <div class="lb-col lb-tiny-24 lb-mid-24"> <h2 id="What_are_the_limitations_of_generative_AI.3F" class="lb-txt-none lb-txt-40 lb-h2 lb-title"> What are the limitations of generative AI?</h2> <div class="lb-txt-17 lb-rtxt"> <p>Despite their advancements, generative AI systems can sometimes produce inaccurate or misleading information. They rely on patterns and data they were trained on and can reflect biases or inaccuracies inherent in that data. Other concerns related to training data include</p> <p><b>Security</b></p> <p>Data privacy and security concerns arise if proprietary data is used to customize generative AI models. Efforts must be made to ensure that the generative AI tools generate responses that limit unauthorized access to proprietary data. Security concerns also arise if there is a lack of accountability and transparency in how AI models make decisions.<br> <a href="/ai/generative-ai/security/">Learn about the secure approach to generative AI using AWS</a></p> <p><b>Creativity</b></p> <p>While generative AI can produce creative content, it often lacks true originality. The creativity of AI is bounded by the data it has been trained on, leading to outputs that may feel repetitive or derivative. Human creativity, which involves a deeper understanding and emotional resonance, remains challenging for AI to replicate fully.</p> <p><b>Cost</b></p> <p>Training and running generative AI models require substantial computational resources. Cloud-based generative AI models are more accessible and affordable than trying to build new models from scratch.</p> <p><b>Explainability</b></p> <p>Due to their complex and opaque nature, generative AI models are often considered black boxes. Understanding how these models arrive at specific outputs is challenging. Improving interpretability and transparency is essential to increase trust and adoption.</p> </div> </div> </div> <div data-eb-slot="what-are-the-best-practices-in-generative-ai" data-eb-slot-meta="{'version':'1.0','slotId':'what-are-the-best-practices-in-generative-ai','experienceId':'5b0041ec-5bf3-11ee-8c99-0242ac120002','allowBlank':false,'hasAltExp':false,'isRTR':false,'filters':{'limit':7,'query':'(id \u003d \'what-are-the-best-practices-in-generative-ai-heading\' OR id \u003d \'what-are-the-best-practices-in-generative-ai-1\' OR id \u003d \'what-are-the-best-practices-in-generative-ai-2\' OR id \u003d \'what-are-the-best-practices-in-generative-ai-3\' OR id \u003d \'what-are-the-best-practices-in-generative-ai-4\')'}}"> <div data-eb-tpl-n="awsm-rt/rt-qa-sampler" data-eb-tpl-v="1.0.0" data-eb-ce="" data-eb-c-scope="what-are-the-best-practices-in-generative-ai" data-eb-d-scope="DIRECTORIES" data-eb-locale="en-US" data-eb-d9d0c036="" data-eb-ssr-ce="" data-eb-tpl-ns="awsmRT" data-eb-hydrated="pending"> <script type="application/json">{"data":{"items":[{"fields":{"benefitTitle":"Begin with internal applications","id":"product-benefits#what-are-the-best-practices-in-generative-ai-1","customSortOrder":"0","benefitContent":"It’s best to start generative AI adoption with internal application development, focusing on process optimization and employee productivity. You get a more controlled environment to test outcomes while building skills and understanding of the technology. You can test the models extensively and even customize them on internal knowledge sources. This way, your customers have a much better experience when you eventually use the models for external applications."},"metadata":{"tags":[]}},{"fields":{"id":"product-benefits#what-are-the-best-practices-in-generative-ai-heading","customSortOrder":"0","patternTitle":"What are the best practices in generative AI adoption?","subheading":"If your organization wants to implement generative AI solutions, consider the following best practices to enhance your efforts."},"metadata":{"tags":[{"id":"GLOBAL#pattern#pattern-data","name":"pattern-data","namespaceId":"GLOBAL#pattern","description":"pattern-data","metadata":{}}]}},{"fields":{"benefitTitle":"Enhance transparency","id":"product-benefits#what-are-the-best-practices-in-generative-ai-2","customSortOrder":"1","benefitContent":"Clearly communicate about all generative AI applications and outputs so your users know they are interacting with AI and not humans. For instance, the AI can introduce itself as AI, or AI-based search results can be marked and highlighted. That way, your users can use their own discretion when they engage with the content. They may also be more proactive in dealing with any inaccuracies or hidden biases the underlying models may have because of their training data limitations."},"metadata":{"tags":[]}},{"fields":{"benefitTitle":"Implement security","id":"product-benefits#what-are-the-best-practices-in-generative-ai-3","customSortOrder":"2","benefitContent":"Implement guardrails so your generative AI applications don't allow inadvertent unauthorized access to sensitive data. Involve security teams from the start so all aspects can be considered from the beginning. 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The model will also improve continuously through testing, and you get more control over expected outcomes and responses."},"metadata":{"tags":[]}}]},"metadata":{"auth":{},"pagination":{"empty":false,"present":true},"testAttributes":{}},"context":{"page":{"pageUrl":"https://aws.amazon.com/what-is/generative-ai/"},"environment":{"stage":"prod","region":"us-east-1"},"sdkVersion":"1.0.129"},"refMap":{"manifest.js":"3daf852ae2","rt-qa-sampler.js":"a1a586a2a7","rt-qa-sampler.rtl.css":"bd7152f8b0","rt-qa-sampler.css":"c739e31c0a","rt-qa-sampler.css.js":"f965de4043","rt-qa-sampler.rtl.css.js":"ab040a2e3b"},"settings":{"templateMappings":{"patternHeading":"patternTitle","patternSubheading":"subheading","topic":"benefitTitle","content":"benefitContent"}}}</script> <div data-eb-tpl-root="" data-reactroot=""> <span data-eb-item-id="product-benefits#what-are-the-best-practices-in-generative-ai-heading"> <div> <div data-rg-lang="en" data-rg-locale="en-US" lang="en-US" dir="ltr"> <div data-rg-theme="blue" data-rg-mode="light" data-style-count="0"> <div data-rg-n="GridStack" class="gridStack_module_gridStack__35667094"> <div data-rg-n="Grid" class="qaSampler_module_qaSamplerGrid__b180ba78 grid_module_grid__590284f4" style="--rg-grid-gap:var(--rg-margin-3)"> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_colXs12__66940a30 col_module_colS12__66940a30 col_module_colM5__66940a30 col_module_colL5__66940a30 col_module_colXl5__66940a30 col_module_colXxl5__66940a30"> <div data-testid="sticky-header" class="stickyHeader_module_stickyHeader__91e4d062" style="top:0px"> <h2 data-rg-n="HeadingText" class="basetext_module_text__61099e5e headingtext_module_heading__00ee2f90 headingtext_module_size2__00ee2f90">What are the best practices in generative AI adoption?</h2> <h5 data-rg-n="SubheadingText" class="basetext_module_text__61099e5e subheadingtext_module_subheading__99e66897 subheadingtext_module_size2__99e66897">If your organization wants to implement generative AI solutions, consider the following best practices to enhance your efforts.</h5> </div> </div> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_colXs0__66940a30 col_module_colS0__66940a30 col_module_colM1__66940a30 col_module_colL1__66940a30 col_module_colXl1__66940a30 col_module_colXxl1__66940a30"></div> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_colXs12__66940a30 col_module_colS12__66940a30 col_module_colM6__66940a30 col_module_colL6__66940a30 col_module_colXl6__66940a30 col_module_colXxl6__66940a30 qaSampler_module_qaSamplerExpanderCol__b180ba78"> <div class="itemExpander_module_expandableSection__e56d1e98" data-eb-item-id="0"> <div class="itemExpander_module_expandableSectionHeader__e56d1e98 qaSampler_module_qaSamplerExpanderHeader__b180ba78" id="0-trigger" aria-controls="content-0" aria-expanded="false" tabindex="0" role="button" data-rigel-analytics="{"name":"ItemExpander","properties":{"expanded":false}}"> <h3 data-rg-n="TitleText" class="basetext_module_text__61099e5e titletext_module_title__e23b0698 qaSampler_module_qaSamplerTitle__b180ba78 titletext_module_size4__e23b0698">Begin with internal applications</h3> <div class="itemExpander_module_expandableSectionHeaderTrigger__e56d1e98" aria-hidden="true"> <span class="icon_module_icon__31e1c712 icon_module_sizeS__31e1c712"> <svg role="presentation" xmlns="http://www.w3.org/2000/svg" viewbox="0 0 16 16"> <path d="M16 9H9V16H7V9H0V7H7V0H9V7H16V9Z" data-type="fill"></path> </svg></span> </div> </div> <div id="content-0" aria-labelledby="0-trigger" class="itemExpander_module_expandableSectionContent__e56d1e98"> <div class="itemExpander_module_expandableSectionContentWrapper__e56d1e98"> <div data-rg-n="BodyText" class="basetext_module_text__61099e5e bodytext_module_body__ca32720c qaSampler_module_qaSamplerContent__b180ba78 bodytext_module_size1__ca32720c"> It’s best to start generative AI adoption with internal application development, focusing on process optimization and employee productivity. You get a more controlled environment to test outcomes while building skills and understanding of the technology. You can test the models extensively and even customize them on internal knowledge sources. This way, your customers have a much better experience when you eventually use the models for external applications. </div> </div> </div> </div> <div class="itemExpander_module_expandableSection__e56d1e98" data-eb-item-id="1"> <div class="itemExpander_module_expandableSectionHeader__e56d1e98 qaSampler_module_qaSamplerExpanderHeader__b180ba78" id="1-trigger" aria-controls="content-1" aria-expanded="false" tabindex="0" role="button" data-rigel-analytics="{"name":"ItemExpander","properties":{"expanded":false}}"> <h3 data-rg-n="TitleText" class="basetext_module_text__61099e5e titletext_module_title__e23b0698 qaSampler_module_qaSamplerTitle__b180ba78 titletext_module_size4__e23b0698">Enhance transparency</h3> <div class="itemExpander_module_expandableSectionHeaderTrigger__e56d1e98" aria-hidden="true"> <span class="icon_module_icon__31e1c712 icon_module_sizeS__31e1c712"> <svg role="presentation" xmlns="http://www.w3.org/2000/svg" viewbox="0 0 16 16"> <path d="M16 9H9V16H7V9H0V7H7V0H9V7H16V9Z" data-type="fill"></path> </svg></span> </div> </div> <div id="content-1" aria-labelledby="1-trigger" class="itemExpander_module_expandableSectionContent__e56d1e98"> <div class="itemExpander_module_expandableSectionContentWrapper__e56d1e98"> <div data-rg-n="BodyText" class="basetext_module_text__61099e5e bodytext_module_body__ca32720c qaSampler_module_qaSamplerContent__b180ba78 bodytext_module_size1__ca32720c"> Clearly communicate about all generative AI applications and outputs so your users know they are interacting with AI and not humans. For instance, the AI can introduce itself as AI, or AI-based search results can be marked and highlighted. That way, your users can use their own discretion when they engage with the content. They may also be more proactive in dealing with any inaccuracies or hidden biases the underlying models may have because of their training data limitations. </div> </div> </div> </div> <div class="itemExpander_module_expandableSection__e56d1e98" data-eb-item-id="2"> <div class="itemExpander_module_expandableSectionHeader__e56d1e98 qaSampler_module_qaSamplerExpanderHeader__b180ba78" id="2-trigger" aria-controls="content-2" aria-expanded="false" tabindex="0" role="button" data-rigel-analytics="{"name":"ItemExpander","properties":{"expanded":false}}"> <h3 data-rg-n="TitleText" class="basetext_module_text__61099e5e titletext_module_title__e23b0698 qaSampler_module_qaSamplerTitle__b180ba78 titletext_module_size4__e23b0698">Implement security</h3> <div class="itemExpander_module_expandableSectionHeaderTrigger__e56d1e98" aria-hidden="true"> <span class="icon_module_icon__31e1c712 icon_module_sizeS__31e1c712"> <svg role="presentation" xmlns="http://www.w3.org/2000/svg" viewbox="0 0 16 16"> <path d="M16 9H9V16H7V9H0V7H7V0H9V7H16V9Z" data-type="fill"></path> </svg></span> </div> </div> <div id="content-2" aria-labelledby="2-trigger" class="itemExpander_module_expandableSectionContent__e56d1e98"> <div class="itemExpander_module_expandableSectionContentWrapper__e56d1e98"> <div data-rg-n="BodyText" class="basetext_module_text__61099e5e bodytext_module_body__ca32720c qaSampler_module_qaSamplerContent__b180ba78 bodytext_module_size1__ca32720c"> Implement guardrails so your generative AI applications don't allow inadvertent unauthorized access to sensitive data. Involve security teams from the start so all aspects can be considered from the beginning. For example, you may have to mask data and remove personally identifiable information (PII) before you train any models on internal data. </div> </div> </div> </div> <div class="itemExpander_module_expandableSection__e56d1e98" data-eb-item-id="3"> <div class="itemExpander_module_expandableSectionHeader__e56d1e98 qaSampler_module_qaSamplerExpanderHeader__b180ba78" id="3-trigger" aria-controls="content-3" aria-expanded="false" tabindex="0" role="button" data-rigel-analytics="{"name":"ItemExpander","properties":{"expanded":false}}"> <h3 data-rg-n="TitleText" class="basetext_module_text__61099e5e titletext_module_title__e23b0698 qaSampler_module_qaSamplerTitle__b180ba78 titletext_module_size4__e23b0698">Test extensively</h3> <div class="itemExpander_module_expandableSectionHeaderTrigger__e56d1e98" aria-hidden="true"> <span class="icon_module_icon__31e1c712 icon_module_sizeS__31e1c712"> <svg role="presentation" xmlns="http://www.w3.org/2000/svg" viewbox="0 0 16 16"> <path d="M16 9H9V16H7V9H0V7H7V0H9V7H16V9Z" data-type="fill"></path> </svg></span> </div> </div> <div id="content-3" aria-labelledby="3-trigger" class="itemExpander_module_expandableSectionContent__e56d1e98"> <div class="itemExpander_module_expandableSectionContentWrapper__e56d1e98"> <div data-rg-n="BodyText" class="basetext_module_text__61099e5e bodytext_module_body__ca32720c qaSampler_module_qaSamplerContent__b180ba78 bodytext_module_size1__ca32720c"> Develop automated and manual testing processes to validate results and test all types of scenarios the generative AI system may experience. Have different groups of beta testers who try out the applications in different ways and document results. The model will also improve continuously through testing, and you get more control over expected outcomes and responses. </div> </div> </div> </div> </div> </div> </div> </div> </div> </div></span> </div> </div> </div> <div data-eb-slot="dev-a-classic-card-filter-2" data-eb-slot-meta="{'version':'1.0','slotId':'dev-a-classic-card-filter-2','experienceId':'d415ae75-d312-4d41-ae58-828472548e23','allowBlank':true,'hasAltExp':false,'isRTR':false,'filters':{'limit':100,'query':'(id \u003d \'classic-cards-filter-search-pattern-data-gen-ai-sub-page-tools-what\' OR id \u003d \'classic-cards-filter-search-pattern-data-genai-sub-page-tools-1\' OR id \u003d \'classic-cards-filter-search-pattern-data-genai-sub-page-tools-2\' OR id \u003d \'classic-cards-filter-search-pattern-data-genai-sub-page-tools-3\' OR id \u003d \'classic-cards-filter-search-pattern-data-genai-sub-page-tools-4\' OR id \u003d \'classic-cards-filter-search-pattern-data-genai-sub-page-tools-5\' OR id \u003d \'classic-cards-filter-search-pattern-data-genai-sub-page-tools-6a\')'}}"> <div data-eb-tpl-n="awsm-rt/rt-relevant-content-collection" data-eb-tpl-v="1.1.0" data-eb-ce="" data-eb-c-scope="dev-a-classic-card-filter-2" data-eb-d-scope="DIRECTORIES" data-eb-locale="en-US" data-eb-f5d5a09e="" data-eb-ssr-ce="" data-eb-tpl-ns="awsmRT" data-eb-hydrated="pending"> <script type="application/json">{"data":{"items":[{"fields":{"patternCtaLabel":"Get started with generative AI on AWS by creating an account today!","customSortOrder":"0","patternTitle":"How can AWS help Generative AI?","patternSubheading":"Amazon Web Services (AWS) makes it easy to build and scale generative AI applications for your data, use cases, and customers. 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With generative AI on AWS, you get enterprise-grade security and privacy, access to industry-leading FMs, generative AI-powered applications, and a data-first approach.</p> </div> <div data-rg-n="Col" class="col_module_col__66940a30 col_module_colXs12__66940a30 col_module_colS12__66940a30 col_module_colM4__66940a30 col_module_colL4__66940a30 col_module_colXl4__66940a30 col_module_colXxl4__66940a30 templateHeader_module_templateLink__b496fe91"> <a data-rg-n="Link" href="https://portal.aws.amazon.com/billing/signup" rel="noopener noreferrer" target="_blank" data-rigel-analytics="{"name":"Link","properties":{"monochrome":true,"size":1}}" class="basetext_module_text__61099e5e bodytext_module_body__ca32720c link_module_link__f23c9474 link_module_monochrome__f23c9474 bodytext_module_size1__ca32720c bodytext_module_link__ca32720c">Get started with generative AI on AWS by creating an account today!</a> </div> </div> </div> </div> </div> <div data-rg-n="Grid" class="grid_module_grid__590284f4" 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They are capable of performing a wide variety of general tasks like answering questions, writing essays, and captioning images. </div> </div> </div> </div> <div class="itemExpander_module_expandableSection__e56d1e98" data-eb-item-id="1"> <div class="itemExpander_module_expandableSectionHeader__e56d1e98 qaSampler_module_qaSamplerExpanderHeader__b180ba78" id="1-trigger" aria-controls="content-1" aria-expanded="false" tabindex="0" role="button" data-rigel-analytics="{"name":"ItemExpander","properties":{"expanded":false}}"> <h3 data-rg-n="TitleText" class="basetext_module_text__61099e5e titletext_module_title__e23b0698 qaSampler_module_qaSamplerTitle__b180ba78 titletext_module_size4__e23b0698">When was generative AI created?</h3> <div class="itemExpander_module_expandableSectionHeaderTrigger__e56d1e98" aria-hidden="true"> <span class="icon_module_icon__31e1c712 icon_module_sizeS__31e1c712"> <svg role="presentation" xmlns="http://www.w3.org/2000/svg" viewbox="0 0 16 16"> <path d="M16 9H9V16H7V9H0V7H7V0H9V7H16V9Z" data-type="fill"></path> </svg></span> </div> </div> <div id="content-1" aria-labelledby="1-trigger" class="itemExpander_module_expandableSectionContent__e56d1e98"> <div class="itemExpander_module_expandableSectionContentWrapper__e56d1e98"> <div data-rg-n="BodyText" class="basetext_module_text__61099e5e bodytext_module_body__ca32720c qaSampler_module_qaSamplerContent__b180ba78 bodytext_module_size1__ca32720c"> Generative AI emerged in the late 2010s with advancements in deep learning, particularly with models like Generative Adversarial Networks (GANs) and transformers. Advances in cloud computing have made generative AI commercially viable and available since 2022. </div> </div> </div> </div> <div class="itemExpander_module_expandableSection__e56d1e98" data-eb-item-id="2"> <div class="itemExpander_module_expandableSectionHeader__e56d1e98 qaSampler_module_qaSamplerExpanderHeader__b180ba78" id="2-trigger" aria-controls="content-2" aria-expanded="false" tabindex="0" role="button" data-rigel-analytics="{"name":"ItemExpander","properties":{"expanded":false}}"> <h3 data-rg-n="TitleText" class="basetext_module_text__61099e5e titletext_module_title__e23b0698 qaSampler_module_qaSamplerTitle__b180ba78 titletext_module_size4__e23b0698">What is the difference between AI and Generative AI?</h3> <div class="itemExpander_module_expandableSectionHeaderTrigger__e56d1e98" aria-hidden="true"> <span class="icon_module_icon__31e1c712 icon_module_sizeS__31e1c712"> <svg role="presentation" xmlns="http://www.w3.org/2000/svg" viewbox="0 0 16 16"> <path d="M16 9H9V16H7V9H0V7H7V0H9V7H16V9Z" data-type="fill"></path> </svg></span> </div> </div> <div id="content-2" aria-labelledby="2-trigger" class="itemExpander_module_expandableSectionContent__e56d1e98"> <div class="itemExpander_module_expandableSectionContentWrapper__e56d1e98"> <div data-rg-n="BodyText" class="basetext_module_text__61099e5e bodytext_module_body__ca32720c qaSampler_module_qaSamplerContent__b180ba78 bodytext_module_size1__ca32720c"> Artificial intelligence is the broader concept of making machines more human-like. It includes everything from smart assistants like Alexa, chatbots, and image generators to robotic vacuum cleaners and self-driving cars. 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