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format-standard has-post-thumbnail category-news" itemscope itemtype="http://data-vocabulary.org/NewsArticle"> <div class="image limited-width text_width"> <img src="https://opendatascience.com/wp-content/uploads/2024/01/Untitled-design-12.png" class="full-post-thumb" alt="MIT Researchers Poised To Change AI Training with Efficient New Algorithm"/> <img src="https://opendatascience.com/wp-content/uploads/2024/01/Untitled-design-12.png" class="mobile-post-thumb" alt="MIT Researchers Poised To Change AI Training with Efficient New Algorithm"/> <div> <div class="social"> <div class="post-image-sharrre"> <div class="shareme" data-image="https://opendatascience.com/wp-content/uploads/2024/01/Untitled-design-12.png" data-url="https://opendatascience.com/mit-researchers-poised-to-change-ai-training-with-efficient-new-algorithm/" data-text="MIT Researchers Poised To Change AI Training with Efficient New Algorithm"></div> </div></div> </div> </div> <div class="title"> <h1 id="intro"><a href="https://opendatascience.com/mit-researchers-poised-to-change-ai-training-with-efficient-new-algorithm/" class="entry-title" itemprop="headline">MIT Researchers Poised To Change AI Training with Efficient New Algorithm</a></h1> <p> <a href="https://opendatascience.com/category/news/" title="AI and Data Science News" class="tag-default">AI and Data Science News</a>posted by <a href="https://opendatascience.com/user/admin_odsc/" title="AI and Data Science News" class="tag-defaultic">ODSC Team</a> <span class="legend-default"> <i class="fa fa-clock-o"></i><span class="updated" >November 22, 2024</span> <i class="fa fa-user"></i><span ><a href="" class="author url fn">ODSC Team</a></span> </span> </p> </div> <span class="item-summary-hidden" itemprop="description">Researchers at MIT have developed a new algorithm that significantly improves the efficiency and reliability of training AI systems for complex...</span> <img class="item-image-hidden" itemprop="image" src="https://opendatascience.com/wp-content/uploads/2024/01/Untitled-design-12.png" alt="MIT Researchers Poised To Change AI Training with Efficient New Algorithm" /> <div class="post" itemprop="articleBody"><style type="text/css" data-type="vc_custom-css">.dashed{ border: 1px dashed #242c66;margin-left:0px!important;margin-right:0px!important;} .aio-icon-title { font-size: 28px!important; line-height: 38px!important; margin-top: 15px!important; } button.ubtn-normal { padding: 15px 30px; } .aio-icon{color: #242c66; font-size: 32px; display: inline-block;} .default-icon .aio-icon-default .aio-icon, .default-icon .aio-icon-default .aio-icon-img { margin-right: 15px; } .default-icon .aio-icon-default { display: table-cell; vertical-align: middle; } .default-icon .aio-icon-header { display: table-cell; vertical-align: middle; }</style><p>Researchers at MIT have <a href="https://news.mit.edu/2024/mit-researchers-develop-efficiency-training-more-reliable-ai-agents-1122">developed</a> a new algorithm that significantly improves the efficiency and reliability of training AI systems for complex tasks. This new MIT AI training algorithm addresses challenges faced by reinforcement learning models, a foundational technology in AI decision-making, particularly in scenarios involving variable environments like urban traffic management.</p> <p>AI systems capable of making intelligent decisions have applications across diverse fields such as <a href="https://opendatascience.com/exciting-robotics-and-ai-developments-coming-to-odsc-west-2024/">robotics</a>, medicine, and urban planning. For instance, optimizing traffic flow in a congested city with AI could enhance safety, sustainability, and efficiency.</p> <h3>Current Limitations and Solution</h3> <p>However, training AI to handle such diverse and variable tasks has historically been challenging. Existing reinforcement learning models often falter when confronted with slight variations in their training parameters, such as differences in traffic patterns or speed limits across intersections.</p> <p>To overcome these limitations, the MIT team introduced<a href="https://arxiv.org/pdf/2408.04498"> Model-Based Transfer Learning (MBTL)</a>, a method that strategically selects the most impactful tasks for training. This approach ensures the AI system can generalize effectively across a wide range of related tasks while significantly reducing training costs.</p> <p>“<em>An algorithm that is not very complicated stands a better chance of being adopted by the community because it is easier to implement and easier for others to understand,</em>” said senior author Cathy Wu, a career development associate professor at MIT.</p> <p>Wu collaborated with lead author Jung-Hoon Cho and graduate students Vindula Jayawardana and Sirui Li on this research, which will be presented at the prestigious Neural Information Processing Systems conference.</p> <div class="at-above-post addthis_tool" data-url="https://opendatascience.com/mit-researchers-poised-to-change-ai-training-with-efficient-new-algorithm/"></div><div class="vc_row wpb_row vc_row-fluid dashed"><div class="wpb_column vc_column_container vc_col-sm-12"><div class="vc_column-inner"><div class="wpb_wrapper"><div class="aio-icon-component style_1"><div id="Info-box-wrap-1674" class="aio-icon-box default-icon" style="" ><div class="aio-icon-default"><div class="ult-just-icon-wrapper "><div class="align-icon" style="text-align:center;"> <div class="aio-icon none " style="color:#242c66;font-size:32px;display:inline-block;"> <i class="Defaults-calendar"></i> </div></div></div></div><div class="aio-icon-header" ><a class="aio-icon-box-link" ><h3 class="aio-icon-title ult-responsive" data-ultimate-target='#Info-box-wrap-1674 .aio-icon-title' data-responsive-json-new='{"font-size":"desktop:28px;","line-height":"desktop:38px;"}' style="color:#242c66;">The ODSC East 2025 Black Friday deal ends FRIDAY! Save 75% this week only.</h3></a></div> <div class="aio-icon-description ult-responsive" data-ultimate-target='#Info-box-wrap-1674 .aio-icon-description' data-responsive-json-new='{"font-size":"","line-height":""}' style=""> <h4>In-person conference | <strong>May 13th-15th, 2025 | Boston, MA</strong></h4> <p><span style="font-weight: 400;">Join us on May 13th-15th, 2025, for 3 days of immersive learning and networking with AI experts.</span></p> <p><span style="font-weight: 400;">🔹 World-class AI experts</span></p> <p><span style="font-weight: 400;">🔹 Cutting-edge workshops</span></p> <p><span style="font-weight: 400;">🔹 Hands-on Training</span></p> <p><span style="font-weight: 400;">🔹 Strategic Insights</span></p> <p><span style="font-weight: 400;">🔹 Thought Leadership</span></p> <p><span style="font-weight: 400;">🔹 And much more!</span></p> <p><a href="https://odsc.com/boston/" target="_blank" rel="noopener"><img fetchpriority="high" decoding="async" class="aligncenter wp-image-49163 size-full" src="https://opendatascience.com/wp-content/uploads/2023/07/EAST2024_Black-Friday_728x90-30.png" alt="" width="728" height="90" srcset="https://opendatascience.com/wp-content/uploads/2023/07/EAST2024_Black-Friday_728x90-30.png 728w, https://opendatascience.com/wp-content/uploads/2023/07/EAST2024_Black-Friday_728x90-30-300x37.png 300w" sizes="(max-width: 728px) 100vw, 728px" /></a></div> </div> </div> <div class=" ubtn-ctn-center "><a class="ubtn-link ult-adjust-bottom-margin ubtn-center ubtn-normal " href="https://odsc.com/boston/" target="_blank" rel="noopener" ><button type="button" id="ubtn-7764" class="ubtn ult-adjust-bottom-margin ult-responsive ubtn-normal ubtn-no-hover-bg none ubtn-shd ubtn-center shd-bottom tooltip-6745de2e46e66" data-hover="" data-border-color="" data-bg="#242c66" data-hover-bg="" data-border-hover="" data-shadow-hover="0 5px ;" data-shadow-click="none" data-shadow="0 5px ;" data-shd-shadow="5" data-ultimate-target='#ubtn-7764' data-responsive-json-new='{"font-size":"","line-height":""}' style="font-weight:normal;border:none;box-shadow: 0 5px ;background: #242c66;color: #ffffff;"><span class="ubtn-hover" style="background-color:"></span><span class="ubtn-data ubtn-text " >REGISTER NOW</span></button></a></div><div class="vc_empty_space" style="height: 25px"><span class="vc_empty_space_inner"></span></div></div></div></div></div> <div class="at-below-post addthis_tool" data-url="https://opendatascience.com/mit-researchers-poised-to-change-ai-training-with-efficient-new-algorithm/"></div><style type="text/css" data-type="rcb-_wpb_post_custom_css-43608">.dashed{ border: 1px dashed #242c66;margin-left:0px!important;margin-right:0px!important;} .aio-icon-title { font-size: 28px!important; line-height: 38px!important; margin-top: 15px!important; } button.ubtn-normal { padding: 15px 30px; } .aio-icon{color: #242c66; font-size: 32px; display: inline-block;} .default-icon .aio-icon-default .aio-icon, .default-icon .aio-icon-default .aio-icon-img { margin-right: 15px; } .default-icon .aio-icon-default { display: table-cell; vertical-align: middle; } .default-icon .aio-icon-header { display: table-cell; vertical-align: middle; }</style> <p> </p> <h3>Bridging the Gap in AI Training</h3> <p>Traditional methods for <a href="https://opendatascience.com/hugging-faces-cosmopedia-hopes-to-reshape-pre-training-data/">training AI</a> in dynamic environments often force engineers to choose between two inefficient extremes. They can either train individual algorithms for each task, which demands extensive computational resources or develop a single generalized algorithm for all tasks, which often leads to suboptimal performance.</p> <p>MBTL provides a balanced solution by training on a subset of tasks chosen to maximize performance gains across the broader task spectrum. It leverages zero-shot transfer learning, enabling a model trained on a specific task to perform well on similar, untrained tasks.</p> <p>Wu explained, “<em>We wondered if we could get away with training on a subset of tasks, apply the result to all tasks, and still see a performance increase. The results exceeded our expectations.</em>”</p> <h3>Boosting Efficiency with MBTL</h3> <p>When tested on simulated tasks, including traffic signal management and real-time speed advisories, MBTL proved five to 50 times more efficient than standard training methods. This efficiency enables the model to achieve equivalent performance using significantly less data.</p> <p>For example, training on just two tasks with MBTL can yield results comparable to training on 100 tasks with conventional methods. These efficiency gains translate to reduced <a href="https://opendatascience.com/how-to-use-large-ai-models-at-low-costs/">computational costs</a> and faster training times, paving the way for AI systems to tackle increasingly complex real-world challenges.</p> <p>Looking ahead, the MIT researchers aim to extend MBTL to more intricate problems, including high-dimensional task spaces. Their ultimate goal is to apply this methodology to next-generation mobility systems and other critical areas where AI can drive innovation.</p> <p>This research received support from the National Science Foundation CAREER Award, the Kwanjeong Educational Foundation, and an Amazon Robotics PhD Fellowship.</p> </div> </div> <div class="post-sharrre"> <div class="sharrre-twitter" data-url="https://opendatascience.com/mit-researchers-poised-to-change-ai-training-with-efficient-new-algorithm/" data-text="MIT Researchers Poised To Change AI Training with Efficient New Algorithm" data-title="Tweet"></div> <div class="sharrre-facebook" data-url="https://opendatascience.com/mit-researchers-poised-to-change-ai-training-with-efficient-new-algorithm/" data-text="MIT Researchers Poised To Change AI Training with Efficient New Algorithm" data-title="Share"></div> <div class="sharrre-pinterest" data-url="https://opendatascience.com/mit-researchers-poised-to-change-ai-training-with-efficient-new-algorithm/" data-text="MIT Researchers Poised To Change AI Training with Efficient New Algorithm" data-title="Pin"></div> <div class="sharrre-linkedin" data-url="https://opendatascience.com/mit-researchers-poised-to-change-ai-training-with-efficient-new-algorithm/" data-text="MIT Researchers Poised To Change AI Training with Efficient New Algorithm" data-title="Share"></div> <div class="sharrre-googleplus" data-url="https://opendatascience.com/mit-researchers-poised-to-change-ai-training-with-efficient-new-algorithm/" data-text="MIT Researchers Poised To Change AI Training with Efficient New Algorithm" data-title="+1"></div> </div>  <div class="about-author" id="about-author"> <div class="title-default"> <a href="#" class="active">About author</a> </div> <div class="about"> <img src="https://opendatascience.com/wp-content/uploads/ultimatemember/39/profile_photo-190.png?1732614110" class="gravatar avatar avatar-117 um-avatar um-avatar-uploaded" width="117" height="117" alt="ODSC Team" data-default="https://opendatascience.com/wp-content/plugins/ultimate-member/assets/img/default_avatar.jpg" onerror="if(!this.getAttribute('data-load-error')){this.setAttribute('data-load-error', '1');this.setAttribute('src', this.getAttribute('data-default'));}" /> <h2> <a href="https://opendatascience.com/user/admin_odsc/" title="ODSC Speaker" class="tag-defaultic">ODSC Team</a> </h2> <p>ODSC gathers the attendees, presenters, and companies that are shaping the present and future of data science and AI. 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