时间：2022-12-30 14:00 - 14:20
地点：北京，第四范式大厦12层活动简介
第一届“牛客松”创新技术大赛由蒙牛集团主办，第四范式、海创汇承办，阿里云、德勤、华为云、SAP、中国电信、联想、阿里巴巴CITRUS、深信服、金蝶、奇秦、汉得、富勒、怀信、云徙、创业邦、36氪联合支持。本次大赛以“数智蒙牛 向新而行”为主题，旨在挖掘培养在行业数智化领域具有高成长潜力与战略思维的国内外优秀创新技术团队，共同打造乳业数智化创新标杆，加速科技创新与食品产业融合发展。
主办方为本次参赛者不仅设立了60万现金奖金池，同时还提供“蒙牛商业订单+资本注资+创业辅导+宣传”等重磅权益包，精准赋能。本次大赛将于2021年11月11日正式启动，开设了新零售、智慧牧场、大健康三大赛道。欢迎物联网、人工智能、大数据等领域的优秀个人和企业踊跃报名。
日程安排
日程安排：报名与项目征集
1. 报名及项目提交时间：从2021年11月11日起，截止至12月17日24时。
2. 参赛者通过大赛官网注册报名及上传创新技术解决方案（参考三大赛道方向及评审标准），并确保报名信息准确有效。
3. 另可提交【产品/Demo】和【补充资料】进行更多信息展示，具体要求详见“作品要求”。
4. 参赛对象：个人、企业、研究机构、科研院所均可参加并自由组队，每支队伍不超过5人，且需指定一名队长。
2022/12/21
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09:00 AM - 12:00 PM
数字化转型
演讲嘉宾
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刘杰
数字化负责人
Graph-structured data have been ubiquitous in real-world, such as social networks, scholar networks, knowledge graph etc. Graph representation learning has been a very hot topic, and the goal is to learn low-dimensional representation.Graph-structured data have been ubiquitous in real-wor
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刘杰
数字化负责人
Graph-structured data have been ubiquitous in real-world, such as social networks, scholar networks, knowledge graph etc. Graph representation learning has been a very hot topic, and the goal is to learn low-dimensional representation.Graph-structured data have been ubiquitous in real-world, suc
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刘杰
数字化负责人
Graph-structured data have been ubiquitous in real-world, such as social networks, scholar networks, knowledge graph etc. Graph representation learning has been a very hot topic, and the goal is to learn low-dimensional representation.Graph-structured data have been ubiquitous in real-
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刘杰
数字化负责人
Graph-structured data have been ubiquitous in real-world, such as social networks, scholar networks, knowledge graph etc. Graph representation learning has been a very hot topic, and the goal is to learn low-dimensional representation.Graph-structured data have been ubiquitous in real-world, such as social ne
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刘杰
数字化负责人
Graph-structured data have been ubiquitous in real-world, such as social networks, scholar networks, knowledge graph etc. Graph representation learning has been a very hot topic, and the goal is to learn low-dimensional representation.Graph-structured data have been ubiquitous in real-world, such as social networks, scholar networks, knowledge graph etc. Graph representation learning
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刘杰
数字化负责人
Graph-structured data have been ubiquitous in real-world, such as social networks, scholar networks, knowledge graph etc. Graph representation learning has been a very hot topic, and the goal is to learn low-dimensional representation.Graph-structured data have been ubiquitous in real-world, such as social networks, scholar networks, knowledge graph etc. Graph representation learning
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