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Recently, we are beginning to experiment with broadening their reach to adjacent research communities. 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These very successful workshops have had a widespread impact on the Human Language Technology community. Recently, we are beginning to experiment with broadening their reach to adjacent research communities.</p> <p>Each workshop team spends 6 weeks together at CLSP (after some advance preparation), working in close proximity on some challenging problem or a promising solution technique that has not yet been well studied. Many teams have had a lasting influence on subsequent research and practice through the publications, software, and data that they produce. For many workshop participants, the biggest benefit is the interaction with other researchers, seeding new and lasting collaborations.</p> <p>The workshops also contribute to the pool of trained specialists in the fields of speech and natural language processing by providing immersive training to undergraduate and graduate students, allowing researchers from different backgrounds to learn from one another, and educating all workshop participants through guest lectures, participant seminars, and team research updates.</p> <p>Workshop topics are chosen through an unusual process of Interactive Peer Review. Initial 1-page proposals are broadly solicited early the previous fall; those that survive a screening are invited to a weekend retreat where they are presented, debated and fleshed out.</p> <p>The workshop is preceded by a 2-week summer school in Human Language Technology, run for the benefit of the student participants and other attendees. Each day of the summer school features two invited lecturers and an all-afternoon hands-on lab exercise.</p> <h3>Current Workshop</h3> <p><a href="https://www.clsp.jhu.edu/the-11th-frederick-jelinek-memorial-summer-workshop-on-speech-and-language-technology/">2025 Annual Frederick Jelinek Memorial Summer Workshop</a></p> <h3>Past Workshops</h3> <p><a href="https://www.clsp.jhu.edu/2024-jelinek-summer-workshop-on-speech-and-language-technology/">2024 Annual Frederick Jelinek Memorial Summer Workshop</a></p> <p><a href="https://www.clsp.jhu.edu/2023-jelinek-summer-workshop/">2023 Annual Frederick Jelinek Memorial Summer Workshop</a></p> <p><a href="https://www.clsp.jhu.edu/2022-eighth-frederick-jelinek-memorial-summer-workshop/">2022 Annual Frederick Jelinek Memorial Summer Workshop</a></p> <p><a href="https://www.clsp.jhu.edu/workshops/20-workshop/">2020 Annual Frederick Jelinek Memorial Summer Workshop</a></p> <h2 style="text-align: center;">Workshops by Year</h2> <table style="width: 866px; height: 237px;"> <tbody> <tr> <td align="center"><a href="/workshops/19-workshop/">2019</a></td> <td align="center"><a href="/workshops/18-workshop/">2018</a></td> <td align="center"><a href="https://www.lti.cs.cmu.edu/news/jsalt-summer-workshop-comes-cmu">2017</a></td> <td align="center"><a href="/workshops/16-workshop/">2016</a></td> </tr> <tr> <td align="center"><a href="/workshops/15-workshop/">2015</a></td> <td align="center"><a href="/workshops/14-workshop/">2014</a></td> <td align="center"><a href="/workshops/13-workshop/">2013</a></td> <td align="center"><a href="/workshops/12-workshop/">2012</a></td> </tr> <tr> <td align="center"><a href="/workshops/11-workshop/">2011</a></td> <td align="center"><a href="/workshops/10-workshop/">2010</a></td> <td align="center"><a href="/workshops/09-workshop/">2009</a></td> <td align="center"><a href="/workshops/08-workshop/">2008</a></td> </tr> <tr> <td align="center"><a href="/workshops/07-workshop/">2007</a></td> <td align="center"><a href="/workshops/06-workshop/">2006</a></td> <td align="center"><a href="/workshops/05-workshop/">2005</a></td> <td align="center"><a href="/workshops/04-workshop/">2004</a></td> </tr> <tr> <td align="center"><a href="/workshops/03-workshop/">2003</a></td> <td align="center"><a href="/workshops/02-workshop/">2002</a></td> <td align="center"><a href="/workshops/01-workshop/">2001</a></td> <td align="center"><a href="/workshops/00-workshop/">2000</a></td> </tr> <tr> <td align="center"><a href="/workshops/99-workshop/">1999</a></td> <td align="center"><a href="/workshops/97-workshop/">1998</a></td> <td align="center"><a href="/workshops/97-workshop/">1997</a></td> <td align="center"><a href="/workshops/96-workshop/">1996</a></td> </tr> <tr> <td align="center"><a href="/workshops/95-workshop/">1995</a></td> </tr> </tbody> </table> </div> </div> </div> <div class="span4"> <div class="sidebar right-sidebar"> <div id="text-7" class="widget widget_text"> <div class="textwidget"><a href="/cdn-cgi/l/email-protection#7102081c0110311d180205025f1b195f1415044e0204131b1412054c02041302120318131451121d020102141c181f100302">Subscribe to the CLSP Seminars Mailing List</a></div> </div><div id="ai1ec_agenda_widget-6" class="widget widget_ai1ec_agenda_widget"> <h3 class="widget-title">Upcoming Seminars</h3> <style>  </style> <div class="timely ai1ec-agenda-widget-view ai1ec-clearfix"> <div> <div class="ai1ec-date "> <a class="ai1ec-date-title ai1ec-load-view" href="https://www.clsp.jhu.edu/seminars/action~oneday/exact_date~12-2-2024/"> <div class="ai1ec-month">Dec</div> <div class="ai1ec-day">2</div> <div class="ai1ec-weekday">Mon</div> </a> <div class="ai1ec-date-events"> <div class="ai1ec-event ai1ec-event-id-28130 ai1ec-event-instance-id-3479 "> <a href="https://www.clsp.jhu.edu/events/kai-wei-chang-ucla-towards-trustworthy-ai-teaching-machines-through-language-and-rules/?instance_id=3479" class="ai1ec-popup-trigger ai1ec-load-event"> <span class="ai1ec-event-time"> 12:00 pm </span> <span class="ai1ec-event-title"> Kai-Wei Chang (UCLA) “Towards Tr... <span class="ai1ec-event-location" >@ Hackerman Hall B17</span> </span> </a> <div class="ai1ec-popover ai1ec-popup ai1ec-event-instance-id-3479"> <span class="ai1ec-popup-title"> <a href="https://www.clsp.jhu.edu/events/kai-wei-chang-ucla-towards-trustworthy-ai-teaching-machines-through-language-and-rules/?instance_id=3479" class="ai1ec-load-event" >Kai-Wei Chang (UCLA) “Towards Tr...</a> <span class="ai1ec-event-location" >@ Hackerman Hall B17</span> </span> <div class="ai1ec-event-time"> Dec 2 @ 12:00 pm – 1:15 pm </div> <div class="ai1ec-popup-excerpt">Abstract Over the past decades, machine learning has primarily relied on labeled data, with success often depending on the availability of vast, high-quality annotations and the assumption that test conditions mirror training conditions. 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data-nummobile="5" data-channel-subscribers="121 subscribers" data-subscribe-btn="1" data-subscribe-btn-text="Subscribe" data_channel_header_colors ="{"channelName":"","subscribeCount":"","buttonBackground":"","buttonText":""}" data-sby-flags="resizeDisable" data-postid="4109" data-sby-supports-lightbox="1" data-videocardlayout="vertical" > <div class="sby_items_wrap" style="padding: 5px;"> <div class="sby_item sby_new sby_transition" id="sby_UCWWiAEF7PMk-OK0-rwGITIg_4N5EzdJATD4" data-date="1732290698" data-video-id="4N5EzdJATD4"> <div class="sby_inner_item"> <div class="sby_video_thumbnail_wrap sby_item_video_thumbnail_wrap"> <a class="sby_video_thumbnail sby_item_video_thumbnail" href="https://www.youtube.com/watch?v=4N5EzdJATD4" target="_blank" rel="noopener" data-full-res="https://i1.ytimg.com/vi/4N5EzdJATD4/maxresdefault.jpg" data-img-src-set="{"120":"https:\/\/i1.ytimg.com\/vi\/4N5EzdJATD4\/default.jpg","320":"https:\/\/i1.ytimg.com\/vi\/4N5EzdJATD4\/mqdefault.jpg","480":"https:\/\/i1.ytimg.com\/vi\/4N5EzdJATD4\/hqdefault.jpg","640":"https:\/\/i1.ytimg.com\/vi\/4N5EzdJATD4\/sddefault.jpg"}" data-video-id="4N5EzdJATD4" data-video-title="Probabilistic Inference in Language Models via Twisted Sequential Monte Carlo -- Roger Grosse"> <img decoding="async" src="https://www.clsp.jhu.edu/wp-content/plugins/feeds-for-youtube/img/placeholder.png" alt="Abstract Numerous capability and safety techniques of Large Language Models (LLMs), including RLHF, automated red-teaming, prompt engineering, and infilling, can be cast as sampling from an unnormalized target distribution defined by a given reward or potential function over the full sequence. In this work, we leverage the rich toolkit of Sequential Monte Carlo (SMC) for these probabilistic inference problems. In particular, we use learned twist functions to estimate the expected future value of the potential at each timestep, which enables us to focus inference-time computation on promising partial sequences. We propose a novel contrastive method for learning the twist functions, and establish connections with the rich literature of soft reinforcement learning. As a complementary application of our twisted SMC framework, we present methods for evaluating the accuracy of language model inference techniques using novel bidirectional SMC bounds on the log partition function. These bounds can be used to estimate the KL divergence between the inference and target distributions in both directions. We apply our inference evaluation techniques to show that twisted SMC is effective for sampling undesirable outputs from a pretrained model (a useful component of harmlessness training and automated red-teaming), generating reviews with varied sentiment, and performing infilling tasks. Biography Roger Grosse is an Associate Professor of Computer Science at the University of Toronto, Schwartz-Reisman Chair in Technology and Society, founding member of the Vector Institute for Artificial Intelligence, and a Member of Technical Staff on the Alignment Science team at Anthropic. His research focuses on using our understanding of deep learning to improve the safety and alignment of AI systems. He has held the Sloan Research Fellowship, Canada CIFAR AI Chair, and Canada Research Chair."> <div class="sby_thumbnail_hover sby_item_video_thumbnail_hover"> <div class="sby_thumbnail_hover_inner"> <span class="sby_video_title" >Probabilistic Inference in Language Models via Twisted Sequential Monte Carlo -- Roger Grosse</span> </div> </div> <div class="sby_play_btn" > <span class="sby_play_btn_bg"></span> <svg aria-hidden="true" focusable="false" data-prefix="fab" data-icon="youtube" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 576 512" class="svg-inline--fa fa-youtube fa-w-18"><path fill="currentColor" d="M549.655 124.083c-6.281-23.65-24.787-42.276-48.284-48.597C458.781 64 288 64 288 64S117.22 64 74.629 75.486c-23.497 6.322-42.003 24.947-48.284 48.597-11.412 42.867-11.412 132.305-11.412 132.305s0 89.438 11.412 132.305c6.281 23.65 24.787 41.5 48.284 47.821C117.22 448 288 448 288 448s170.78 0 213.371-11.486c23.497-6.321 42.003-24.171 48.284-47.821 11.412-42.867 11.412-132.305 11.412-132.305s0-89.438-11.412-132.305zm-317.51 213.508V175.185l142.739 81.205-142.739 81.201z" class=""></path></svg> </div> <span class="sby_loader sby_hidden" style="background-color: rgb(255, 255, 255);"></span> </a> </div> </div> </div><div class="sby_item sby_new sby_transition" id="sby_UCWWiAEF7PMk-OK0-rwGITIg_cf4apIcnPtU" data-date="1732073606" data-video-id="cf4apIcnPtU"> <div class="sby_inner_item"> <div class="sby_video_thumbnail_wrap sby_item_video_thumbnail_wrap"> <a class="sby_video_thumbnail sby_item_video_thumbnail" href="https://www.youtube.com/watch?v=cf4apIcnPtU" target="_blank" rel="noopener" data-full-res="https://i4.ytimg.com/vi/cf4apIcnPtU/maxresdefault.jpg" data-img-src-set="{"120":"https:\/\/i4.ytimg.com\/vi\/cf4apIcnPtU\/default.jpg","320":"https:\/\/i4.ytimg.com\/vi\/cf4apIcnPtU\/mqdefault.jpg","480":"https:\/\/i4.ytimg.com\/vi\/cf4apIcnPtU\/hqdefault.jpg","640":"https:\/\/i4.ytimg.com\/vi\/cf4apIcnPtU\/sddefault.jpg"}" data-video-id="cf4apIcnPtU" data-video-title="Generative World Explorer"> <img decoding="async" src="https://www.clsp.jhu.edu/wp-content/plugins/feeds-for-youtube/img/placeholder.png" alt="Project page: https://generative-world-explorer.github.io/ Paper: https://arxiv.org/abs/2411.11844 Abstract: Planning with partial observation is a central challenge in embodied AI. A majority of prior works have tackled this challenge by developing agents that physically explore their environment to update their beliefs about the world state. In contrast, humans can imagine unseen parts of the world through a mental exploration and revise their beliefs with imagined observations. Such updated beliefs can allow them to make more informed decisions, without necessitating the physical exploration of the world at all times. To achieve this human-like ability, we introduce the Generative World Explorer (Genex), an egocentric world exploration framework that allows an agent to mentally explore a large-scale 3D world (e.g., urban scenes) and acquire imagined observations to update its belief. This updated belief will then help the agent to make a more informed decision at the current step. To train Genex, we create a synthetic urban scene dataset, Genex-DB. Our experimental results demonstrate that (1) Genex can generate high-quality and consistent observations during long-horizon exploration of a large virtual physical world and (2) the beliefs updated with the generated observations can inform an existing decision-making model (e.g., an LLM agent) to make better plans."> <div class="sby_thumbnail_hover sby_item_video_thumbnail_hover"> <div class="sby_thumbnail_hover_inner"> <span class="sby_video_title" >Generative World Explorer</span> </div> </div> <div class="sby_play_btn" > <span class="sby_play_btn_bg"></span> <svg aria-hidden="true" focusable="false" data-prefix="fab" data-icon="youtube" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 576 512" class="svg-inline--fa fa-youtube fa-w-18"><path fill="currentColor" d="M549.655 124.083c-6.281-23.65-24.787-42.276-48.284-48.597C458.781 64 288 64 288 64S117.22 64 74.629 75.486c-23.497 6.322-42.003 24.947-48.284 48.597-11.412 42.867-11.412 132.305-11.412 132.305s0 89.438 11.412 132.305c6.281 23.65 24.787 41.5 48.284 47.821C117.22 448 288 448 288 448s170.78 0 213.371-11.486c23.497-6.321 42.003-24.171 48.284-47.821 11.412-42.867 11.412-132.305 11.412-132.305s0-89.438-11.412-132.305zm-317.51 213.508V175.185l142.739 81.205-142.739 81.201z" class=""></path></svg> </div> <span class="sby_loader sby_hidden" style="background-color: rgb(255, 255, 255);"></span> </a> </div> </div> </div><div class="sby_item sby_new sby_transition" id="sby_UCWWiAEF7PMk-OK0-rwGITIg_d5p6IhagfXM" data-date="1731992425" data-video-id="d5p6IhagfXM"> <div class="sby_inner_item"> <div class="sby_video_thumbnail_wrap sby_item_video_thumbnail_wrap"> <a class="sby_video_thumbnail sby_item_video_thumbnail" href="https://www.youtube.com/watch?v=d5p6IhagfXM" target="_blank" rel="noopener" data-full-res="https://i1.ytimg.com/vi/d5p6IhagfXM/maxresdefault.jpg" data-img-src-set="{"120":"https:\/\/i1.ytimg.com\/vi\/d5p6IhagfXM\/default.jpg","320":"https:\/\/i1.ytimg.com\/vi\/d5p6IhagfXM\/mqdefault.jpg","480":"https:\/\/i1.ytimg.com\/vi\/d5p6IhagfXM\/hqdefault.jpg","640":"https:\/\/i1.ytimg.com\/vi\/d5p6IhagfXM\/sddefault.jpg"}" data-video-id="d5p6IhagfXM" data-video-title="Automatically Deriving Adjectival Scales – Marie-Catherine de Marneffe (OSU) - 2013"> <img decoding="async" src="https://www.clsp.jhu.edu/wp-content/plugins/feeds-for-youtube/img/placeholder.png" alt="December 6, 2013 Abstract In this talk, I will discuss how to automatically derive the orderings and meanings of gradable adjectives. To determine whether the intended answer is “yes” or “no” in a dialogue such as “Was the movie wonderful? It was worth seeing”, we need to evaluate how “worth seeing” relates to “wonderful”. Can we automatically learn from real texts the scalar orderings people assign to these modifiers? I will show how we can exploit the availability of large amounts of text on the web (such as online reviews ratings) to approximate these orderings. Then I will turn to neural network language models. I will show that continuous space word representations extracted from such models can be used to derive adjectival scales of high quality, emphasizing that neural network language models do capture semantic regularities. I evaluate the quality of the adjectival scales on several datasets. Next, I will briefly turn to biomedical data: what does it mean to show “severe symptoms of cardiac disease” or “mild pulmonary symptoms”? I will outline work in progress targeting the meaning of gradable adjectives in that domain. Not only do we want to get an ordering between such adjectives, but we also want to learn what counts as “severe” or “mild” symptoms of a disease. Biography Marie-Catherine de Marneffe is an assistant professor in Linguistics at The Ohio State University. She received her PhD from Stanford University in December 2012 under the supervision of Christopher D. Manning. She is developing computational linguistic methods that capture what is conveyed by speakers beyond the literal meaning of the words they say. Primarily she wants to ground meanings in corpus data, and show how such meanings can drive pragmatic inference. She has also worked on Recognizing Textual Entailment and contributed to defining the Stanford Dependencies representation, which is designed to be a practical representation of grammatical relations and predicate argument structure"> <div class="sby_thumbnail_hover sby_item_video_thumbnail_hover"> <div class="sby_thumbnail_hover_inner"> <span class="sby_video_title" >Automatically Deriving Adjectival Scales – Marie-Catherine de Marneffe (OSU) - 2013</span> </div> </div> <div class="sby_play_btn" > <span class="sby_play_btn_bg"></span> <svg aria-hidden="true" focusable="false" data-prefix="fab" data-icon="youtube" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 576 512" class="svg-inline--fa fa-youtube fa-w-18"><path fill="currentColor" d="M549.655 124.083c-6.281-23.65-24.787-42.276-48.284-48.597C458.781 64 288 64 288 64S117.22 64 74.629 75.486c-23.497 6.322-42.003 24.947-48.284 48.597-11.412 42.867-11.412 132.305-11.412 132.305s0 89.438 11.412 132.305c6.281 23.65 24.787 41.5 48.284 47.821C117.22 448 288 448 288 448s170.78 0 213.371-11.486c23.497-6.321 42.003-24.171 48.284-47.821 11.412-42.867 11.412-132.305 11.412-132.305s0-89.438-11.412-132.305zm-317.51 213.508V175.185l142.739 81.205-142.739 81.201z" class=""></path></svg> </div> <span class="sby_loader sby_hidden" style="background-color: rgb(255, 255, 255);"></span> </a> </div> </div> </div><div class="sby_item sby_new sby_transition" id="sby_UCWWiAEF7PMk-OK0-rwGITIg_uf6PSqy3WPc" data-date="1731645479" data-video-id="uf6PSqy3WPc"> <div class="sby_inner_item"> <div class="sby_video_thumbnail_wrap sby_item_video_thumbnail_wrap"> <a class="sby_video_thumbnail sby_item_video_thumbnail" href="https://www.youtube.com/watch?v=uf6PSqy3WPc" target="_blank" rel="noopener" data-full-res="https://i2.ytimg.com/vi/uf6PSqy3WPc/maxresdefault.jpg" data-img-src-set="{"120":"https:\/\/i2.ytimg.com\/vi\/uf6PSqy3WPc\/default.jpg","320":"https:\/\/i2.ytimg.com\/vi\/uf6PSqy3WPc\/mqdefault.jpg","480":"https:\/\/i2.ytimg.com\/vi\/uf6PSqy3WPc\/hqdefault.jpg","640":"https:\/\/i2.ytimg.com\/vi\/uf6PSqy3WPc\/sddefault.jpg"}" data-video-id="uf6PSqy3WPc" data-video-title="Evaluating Unlearning and Memorization via Compression -- Zhili Feng (CMU)"> <img decoding="async" src="https://www.clsp.jhu.edu/wp-content/plugins/feeds-for-youtube/img/placeholder.png" alt="Zhili Feng: https://zhilif.github.io/"> <div class="sby_thumbnail_hover sby_item_video_thumbnail_hover"> <div class="sby_thumbnail_hover_inner"> <span class="sby_video_title" >Evaluating Unlearning and Memorization via Compression -- Zhili Feng (CMU)</span> </div> </div> <div class="sby_play_btn" > <span class="sby_play_btn_bg"></span> <svg aria-hidden="true" focusable="false" data-prefix="fab" data-icon="youtube" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 576 512" class="svg-inline--fa fa-youtube fa-w-18"><path fill="currentColor" d="M549.655 124.083c-6.281-23.65-24.787-42.276-48.284-48.597C458.781 64 288 64 288 64S117.22 64 74.629 75.486c-23.497 6.322-42.003 24.947-48.284 48.597-11.412 42.867-11.412 132.305-11.412 132.305s0 89.438 11.412 132.305c6.281 23.65 24.787 41.5 48.284 47.821C117.22 448 288 448 288 448s170.78 0 213.371-11.486c23.497-6.321 42.003-24.171 48.284-47.821 11.412-42.867 11.412-132.305 11.412-132.305s0-89.438-11.412-132.305zm-317.51 213.508V175.185l142.739 81.205-142.739 81.201z" class=""></path></svg> </div> <span class="sby_loader sby_hidden" style="background-color: rgb(255, 255, 255);"></span> </a> </div> </div> </div><div class="sby_item sby_new sby_transition" id="sby_UCWWiAEF7PMk-OK0-rwGITIg_X_U_jdXXfv0" data-date="1731004488" data-video-id="X_U_jdXXfv0"> <div class="sby_inner_item"> <div class="sby_video_thumbnail_wrap sby_item_video_thumbnail_wrap"> <a class="sby_video_thumbnail sby_item_video_thumbnail" href="https://www.youtube.com/watch?v=X_U_jdXXfv0" target="_blank" rel="noopener" data-full-res="https://i1.ytimg.com/vi/X_U_jdXXfv0/maxresdefault.jpg" data-img-src-set="{"120":"https:\/\/i1.ytimg.com\/vi\/X_U_jdXXfv0\/default.jpg","320":"https:\/\/i1.ytimg.com\/vi\/X_U_jdXXfv0\/mqdefault.jpg","480":"https:\/\/i1.ytimg.com\/vi\/X_U_jdXXfv0\/hqdefault.jpg","640":"https:\/\/i1.ytimg.com\/vi\/X_U_jdXXfv0\/sddefault.jpg"}" data-video-id="X_U_jdXXfv0" data-video-title="AnaloBench: Benchmarking the Identification of Abstract and Long-context Analogies (EMNLP 2024)"> <img decoding="async" src="https://www.clsp.jhu.edu/wp-content/plugins/feeds-for-youtube/img/placeholder.png" alt="Paper: https://arxiv.org/abs/2402.12370 Abstract: Humans regularly engage in analogical thinking, relating personal experiences to current situations (X is analogous to Y because of Z). Analogical thinking allows humans to solve problems in creative ways, grasp difficult concepts, and articulate ideas more effectively. Can language models (LMs) do the same? To answer this question, we propose AnaloBench, a benchmark to determine analogical reasoning ability in LMs. Our benchmarking approach focuses on aspects of this ability that are common among humans: (i) recalling related experiences from a large amount of information, and (ii) applying analogical reasoning to complex and lengthy scenarios. We test a broad collection of proprietary models (e.g., GPT family, Claude V2) and open source models such as LLaMA2. As in prior results, scaling up LMs results in some performance boosts. Surprisingly, scale offers minimal gains when, (i) analogies involve lengthy scenarios, or (ii) recalling relevant scenarios from a large pool of information, a process analogous to finding a needle in a haystack. We hope these observations encourage further research in this field."> <div class="sby_thumbnail_hover sby_item_video_thumbnail_hover"> <div class="sby_thumbnail_hover_inner"> <span class="sby_video_title" >AnaloBench: Benchmarking the Identification of Abstract and Long-context Analogies (EMNLP 2024)</span> </div> </div> <div class="sby_play_btn" > <span class="sby_play_btn_bg"></span> <svg aria-hidden="true" focusable="false" data-prefix="fab" data-icon="youtube" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 576 512" class="svg-inline--fa fa-youtube fa-w-18"><path fill="currentColor" d="M549.655 124.083c-6.281-23.65-24.787-42.276-48.284-48.597C458.781 64 288 64 288 64S117.22 64 74.629 75.486c-23.497 6.322-42.003 24.947-48.284 48.597-11.412 42.867-11.412 132.305-11.412 132.305s0 89.438 11.412 132.305c6.281 23.65 24.787 41.5 48.284 47.821C117.22 448 288 448 288 448s170.78 0 213.371-11.486c23.497-6.321 42.003-24.171 48.284-47.821 11.412-42.867 11.412-132.305 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