O’REILLY、INTEL AI主办

English中文
将人工智能用起来
2019年6月18-21日
北京,中国
 
紫金大厅B(Grand Hall B)
Add Deep prediction: A year in review for deep learning for time series to your personal schedule
14:00 Deep prediction: A year in review for deep learning for time series Aileen Nielsen (Skillman Consulting)
Add ML ops and Kubeflow pipeline to your personal schedule
14:50 ML ops and Kubeflow pipeline Kaz Sato (Google)
报告厅(Auditorium)
Add AVA: A cloud native deep learning platform at Qiniu to your personal schedule
14:00 AVA: A cloud native deep learning platform at Qiniu Chaoguang Li (Qiniu), Bin Fan (Alluxio)
Add 保险中的机器学习实践 to your personal schedule
16:20 保险中的机器学习实践 鞠芳 (中国人寿研发中心)
多功能厅2(Function Room 2)
Add Achieving Salesforce-scale machine learning in production to your personal schedule
11:15 Achieving Salesforce-scale machine learning in production Sarah Aerni (Salesforce Einstein)
Add Architecting AI applications to your personal schedule
13:10 Architecting AI applications Mikio Braun (Zalando SE)
Add Best practice of building data science platform in Rakuten to your personal schedule
14:00 Best practice of building data science platform in Rakuten Orchlon Ann (Rakuten), TzuLin Chin (Rakuten)
Add AI pipelines on container platform to your personal schedule
14:50 AI pipelines on container platform WEIQIANG ZHUANG (IBM), Huaxin Gao (IBM)
Add Using ML for personalizing food recommendations to your personal schedule
16:20 Using ML for personalizing food recommendations Maulik Soneji (Gojek), Jewel James (Gojek)
多功能厅5A+B(Function Room 5A+B)
Add 自动驾驶技术是如何应用于新潮传媒、新零售行业 to your personal schedule
11:15 自动驾驶技术是如何应用于新潮传媒、新零售行业 Li Yuan (Perceptin 深圳普思英察科技有限公司)
Add How China Telecom combats financial frauds with adversarial autoencoder to your personal schedule
13:10 How China Telecom combats financial frauds with adversarial autoencoder Weisheng Xie (China Telecom BestPay Co., Ltd)
Add 基于目标检测的智能化成矿异常信息提取 to your personal schedule
16:20 基于目标检测的智能化成矿异常信息提取 李苍柏 (中国地质科学院矿产资源研究所)
多功能厅6A+B (Function Room 6A+B)
Add 在边缘实现深度学习 to your personal schedule
13:10 在边缘实现深度学习 陈玉荣 (Intel)
Add PAI tensor accelerator and optimizer: Yet another deep learning compiler to your personal schedule
14:50 PAI tensor accelerator and optimizer: Yet another deep learning compiler 杨军 (阿里巴巴), 龙国平 (Alibaba)
Add 快速社交 (Speed Networking) to your personal schedule
08:00 快速社交 (Speed Networking) | Room: 3楼序厅(3rd Floor Foyer)
Add Friday opening remarks to your personal schedule
08:45 Friday opening remarks | Room: 紫金大厅A(Grand Hall A) Ben Lorica (O'Reilly Media), Roger Chen (Computable), Jason (Jinquan) Dai (Intel)
Add Unifying analytics and AI on big data for faster insights at scale to your personal schedule
08:50 Unifying analytics and AI on big data for faster insights at scale | Room: 紫金大厅A(Grand Hall A) 马子雅 (Ziya Ma) (Intel)
Add Keynote by Hao Zheng to your personal schedule
09:00 Keynote by Hao Zheng | Room: 紫金大厅A(Grand Hall A) Hao Zheng (PlusAI)
Add Keynote by Mikio Braun to your personal schedule
09:15 Keynote by Mikio Braun | Room: 紫金大厅A(Grand Hall A) Mikio Braun (Zalando SE)
Add Keynote with Yangqing Jia to your personal schedule
09:30 Keynote with Yangqing Jia | Room: 紫金大厅A(Grand Hall A) Yangqing Jia (Alibaba Group)
Add Keynote to come to your personal schedule
09:45 Keynote to come | Room: 紫金大厅A(Grand Hall A)
Add Keynote by Michael James to your personal schedule
09:55 Keynote by Michael James | Room: 紫金大厅A(Grand Hall A) Michael James (Cerebras)
Add Keynote to come to your personal schedule
10:15 Keynote to come | Room: 紫金大厅A(Grand Hall A)
Add Keynote by Tim Kraska to your personal schedule
10:20 Keynote by Tim Kraska | Room: 紫金大厅A(Grand Hall A) Tim Kraska (MIT)
Add Closing remarks to your personal schedule
10:40 Closing remarks | Room: 紫金大厅A(Grand Hall A) Ben Lorica (O'Reilly Media), Roger Chen (Computable)
10:45 Morning Break | Room: 报告厅序厅 (Auditorium Foyer)
15:30 Afternoon Break | Room: 报告厅序厅 (Auditorium Foyer)
Add 周五午餐主题桌会 (Friday Topic Tables at Lunch) to your personal schedule
11:55 Lunch 周五午餐主题桌会 (Friday Topic Tables at Lunch) | Room: 彩虹厅及国际厅 (Rainbow Room & Ballroom)
11:15-11:55 (40m) 英文讲话 (Presented in English)
Bringing research and production together with PyTorch 1.0
Joseph Spisak (Facebook)
Learn how PyTorch 1.0 enables you to take state-of-the-art research and deploy it quickly at scale in areas from autonomous vehicles to medical imaging. Joseph Spisak dives deep on the latest updates to the PyTorch framework including TorchScript and the JIT compiler, deployment support, and the C++ interface. He examines how PyTorch 1.0 is utilized at Facebook to power AI across products.
13:10-13:50 (40m) 人工智能对商业及社会的影响 (Impact of AI on Business and Society)
Artificial intelligence meets genomics: Accelerating understanding of our genetic makeup and use of genome editing to revolutionize medicine
Yue Cathy Chang (TutumGene)
Genome editing has been dubbed a top technology that could create trillion-dollar markets. Learn how recent advancements in the application of AI to genomic editing are accelerating transformation of medicine with Yue Cathy Chang as she explores how AI is applied to genome sequencing and editing, the potential to correct mutations, and questions on using genome editing to optimize human health.
14:00-14:40 (40m) 模型与方法 (Models and Methods)
Deep prediction: A year in review for deep learning for time series
Aileen Nielsen (Skillman Consulting)
Catch up on the rapid progress deep learning for time series has made in the use of both convolutional and recurrent neural network architectures. Aileen Nielsen takes you through the state of the art in deep forecasting for 2018 and 2019, including use cases in both forecasting and generating time series.
14:50-15:30 (40m) 实施人工智能 (Implementing AI), 模型与方法 (Models and Methods), 英文讲话 (Presented in English)
ML ops and Kubeflow pipeline
Kaz Sato (Google)
Kaz Sato explains how creating an ML model is just a starting point. To bring the technology into production service, you need to solve various real-world issues such as building a data pipeline for continuous training, automated validation of the model, version control of the model, scalable serving infra, and ongoing operation of the ML infra with monitoring and alerting.
16:20-17:00 (40m) 执行简报/最佳实践 (Executive Briefing/Best Practices), 文化与组织 (Culture and Organization), 案例研究 (Case Studies), 英文讲话 (Presented in English)
AI at ING: The why, how, and what of a data-driven enterprise
Bas Geerdink (ING)
AI is at the core of ING’s business. It is a data-driven enterprise, with analytics skills as a top strategic priority, and is investing in AI, big data, and analytics to improve business processes such as balance forecasting, fraud detection, and customer relation management. Follow along with and be inspired by Bas Geerdink's overview of the use cases and technology.
11:15-11:55 (40m) 实施人工智能 (Implementing AI), 英文讲话 (Presented in English)
Analytics Zoo: Distributed TensorFlow in production on Apache Spark
Yang Wang (Intel)
Building a model is fun and exciting; putting it to production is always a different story. Yang Wang introduces Analytics Zoo, a unified analytics and AI platform for distributed TensorFlow, Keras, and BigDL on Apache Spark, designed for production environment. See how you can benefit from its easy deployment, high performance, and efficient model serving for deep learning applications.
13:10-13:50 (40m) 实施人工智能 (Implementing AI)
Sparkling: 基于Apache Spark进行一站式机器学习
Yiheng Wang (Tencent)
机器学习项目在企业中实际落地往往涉及到复杂工作流构建和数据管理,以及多种工具的整合。而且随着数据规模的增加,团队规模的扩大,这一任务更具挑战性。Apache Spark是业界流行的大数据框架,被广泛的应用在海量数据的分析处理。本议题将介绍我们在腾讯云上如何基于Apache Spark为客户建立一个一站式机器学习平台的相关工作。主要内容包括多种数据源的接入,构建复杂数据管线,利用数据可视化理解数据,通过可插拔的机制使用各种流行的机器学习框架,以及部署和监控模型。我们也会分享在这一过程中遇到的问题和挑战。听众也可以了解到,通过这种和大数据紧密结合的一站式机器学习,用户可以怎样更加高效的建立和管理他们的机器学习项目,从而加速了机器学习在业务中的落地。
14:00-14:40 (40m) 实施人工智能 (Implementing AI)
AVA: A cloud native deep learning platform at Qiniu
Chaoguang Li (Qiniu), Bin Fan (Alluxio)
Atlab Lab at Qiniu Cloud focuses on deep learning for computer vision. Join Chaoguang Li and Bin Fan as they dive deep into a high-performance and cost-effective training platform based on cloud for deep learning called AVA, which deeply integrates open source software stack including TensorFlow, Caffe, Alluxio, and KODO, the company's own cloud object storage.
14:50-15:30 (40m) 实施人工智能 (Implementing AI)
Query the planet: Geospatial big data analytics at Uber
Zhenxiao Luo (Uber)
Learn how Uber uses artificial intelligence to analyze geospatial big data, one of its distinct challenges. Locations and trips provide insights that can improve business decisions and better serve users. Zhenxiao Luo details how geospatial data analysis is particularly challenging. Efficiency, usability, and scalability must be achieved in order to meet user needs and business requirements.
16:20-17:00 (40m) 实施人工智能 (Implementing AI)
保险中的机器学习实践
鞠芳 (中国人寿研发中心)
分析保险行业人工智能发展情况及现有数据特性,评估机器学习模型构建的主流工具、语言、算法。总结基于机器学习技术,实现一个保险业人工智能场景的全流程——从场景研讨、数据加工提取到模型构建、模型效果评估、模型落地实施。以一个真实的机器学习模型项目为例,介绍整个方法论不同环节中各方人员的参与工作内容和比例,探讨特征稳定性、样本不均衡、参数选择、模型可解释性等环节的难点及尝试方案。为金融或者其他行业的机器学习项目落地提供参考和指导。
11:15-11:55 (40m) 执行简报/最佳实践 (Executive Briefing/Best Practices), 英文讲话 (Presented in English)
Achieving Salesforce-scale machine learning in production
Sarah Aerni (Salesforce Einstein)
At Salesforce Einstein, data science is an Agile partner to over 100,000 customers. Sarah Aerni examines the lessons learned in business, technology, and the process undertaken. Hear about use cases, oft-missed foundational elements for deployment, and the evaluations that must happen along the way and learn to achieve and sustain models in production—and where to go from there.
13:10-13:50 (40m) 实施人工智能 (Implementing AI), 英文讲话 (Presented in English)
Architecting AI applications
Mikio Braun (Zalando SE)
Mikio Braun takes you back through the past 20 years of machine learning research to explore aspects of artificial intelligence, then to current examples like autonomous cars and chatbots. Together you'll put together a mental model for a reference architecture for artificial intelligence systems.
14:00-14:40 (40m) 与人工智能交互 (Interacting with AI), 实施人工智能 (Implementing AI), 英文讲话 (Presented in English)
Best practice of building data science platform in Rakuten
Orchlon Ann (Rakuten), TzuLin Chin (Rakuten)
Orchlon Ann and TzuLin Chin explain the Data Science Platform, a suite of tools for exploring data, training models, and running GPU/CPU compute jobs in an isolated container environment. Discover the one-click machine learning environment creation, a powerful job scheduler, and flexible function as a service component that the Data Science Platform provides.
14:50-15:30 (40m) 企业人工智能 (AI in the Enterprise), 实施人工智能 (Implementing AI)
AI pipelines on container platform
WEIQIANG ZHUANG (IBM), Huaxin Gao (IBM)
AI pipelines simplify the lifecycle workflow management and enhance the reproducibility and collaboration for machine learning and deep learning, and you can use a cloud native platform solution for great portability and scalability. Weiqiang Zhuang and Huaxin Gao explore how by combining strengths, AI pipelines on container platforms can help accelerate AI application development and deployment.
16:20-17:00 (40m) 模型与方法 (Models and Methods), 英文讲话 (Presented in English)
Using ML for personalizing food recommendations
Maulik Soneji (Gojek), Jewel James (Gojek)
Hear the story of how Maulik Soneji and Jewel James prototyped the search framework that personalizes the restaurant search results by using machine learning (ML) to learn what constitutes a relevant restaurant given a user's purchasing history.
11:15-11:55 (40m) 实施人工智能 (Implementing AI)
自动驾驶技术是如何应用于新潮传媒、新零售行业
Li Yuan (Perceptin 深圳普思英察科技有限公司)
如何令自动驾驶技术落地并结合新潮传媒以及新零售业务,相关的技术是如何实现,商业模式是什么以及如何通过人工只能技术提升行业的效率。
13:10-13:50 (40m) 案例研究 (Case Studies)
How China Telecom combats financial frauds with adversarial autoencoder
Weisheng Xie (China Telecom BestPay Co., Ltd)
Weisheng Xie dives deep into how China Telecom exploits the good representation capability of adversarial autoencoder (AAE) in risk factors modeling in fighting a special kind of financial frauds. It's one step of a long stack of unsupervised tasks, yet you can learn how it's proved to be efficient and effective in practice.
14:00-14:40 (40m) 与人工智能交互 (Interacting with AI), 实施人工智能 (Implementing AI)
人工智能病理影像辅助诊断系统——从方法到落地
王书浩 (透彻影像)
病理学是医学诊断的“金标准”,病理报告对于临床医生提供进一步治疗策略至关重要。一位能够独立发病理报告的病理医师需要10年以上的培养周期,我国目前共有约1万名注册在案的病理医师,根据WHO的要求,人才缺口为4-9万人。使用人工智能来辅助病理医师对样本进行诊断,不仅能够大幅提高医师的诊断效率,而且可以减少漏诊,提高诊断准确率。数字化的病理影像能够观察到组织的细胞形态,在最高倍数字扫描时,文件尺寸达到GB量级,需要从人工智能和系统工程的层面去应对这些挑战。在这个演讲中,我们将从人工智能系统的构建方法入手,介绍透彻影像与中国人民解放军总医院在消化道病理影像辅助系统研发过程中的技术细节。同时,我们将分享诊断系统从部署到落地使用的一些经验。
14:50-15:30 (40m) 人工智能对商业及社会的影响 (Impact of AI on Business and Society)
运用自动化AI技术打击“智能化”网络欺诈
Hongyu Cui (DataVisor)
AI技术在赋能各个产业的同时,也被网络黑产所利用,使得黑产攻击更加自动化,更加隐蔽,难于检测。 DataVisor在互联网反欺诈领域研究发现,目前黑产的攻击模型呈现以下趋势:攻击方法多样化而变化快,攻击手段趋于模拟正常用户,攻击账号主要来源由大规模注册渐渐转向ATO账号。传统的规则系统和有监督的模型,由于对欺诈案例以及标签数据的强依赖,往往无法及时应对迅速演化的黑产攻击,在反欺诈中一直处于被动防守的状态。DataVisor的无监督算法,通过全局分析,在高维空间聚类,可以在无标签情况下,自动发现大规模关联欺诈团伙。无监督算法在提前预警以及检测快速演变欺诈模式方面体现了显著的优势。
16:20-17:00 (40m) 实施人工智能 (Implementing AI)
基于目标检测的智能化成矿异常信息提取
李苍柏 (中国地质科学院矿产资源研究所)
矿床所在的位置往往伴随着地质、地球物理、地球化学、遥感异常,因此,这些异常所在的位置也往往伴随着矿床的存在。所以,在找矿工作当中,一个重要的过程便是在地、物、化、遥数据中寻找异常,并将其整合,得出该区域成矿的概率,从而推断出靶区所在的位置。但传统方法并未考虑空间中点与点之间的相关关系。而卷积神经网络中的卷积和池化方法,充分考虑了点与点之间的相关关系。但单纯使用卷积神经网络只能进行特征提取,不能圈定异常所在的区域。因此,特将目标检测的相关算法引入其中,从而圈定异常所在的区域。
11:15-11:55 (40m) 实施人工智能 (Implementing AI)
Enlighten the future of games with artificial intelligence
Renjei Li (NetEase Fuxi Lab)
Theoretical AI research isn't a bottleneck in AI, but finding a good application scenario for AI is. Renjei Li examines how gaming is a great scenario for AI, and he walks you through some of the recent research in the future of AI games with reinforcement learning, natural language processing (NLP), computer vision and graphics, and user persona and virtual human.
13:10-13:50 (40m) 模型与方法 (Models and Methods)
在边缘实现深度学习
陈玉荣 (Intel)
深度学习在许多领域尤其是视觉识别/理解方面取得了巨大突破,但它在训练和部署方面都存在一些挑战。本讲座将介绍我们通过高效CNN算法设计、领先DNN模型压缩技术和创新部署时DNN网络结构优化来解决深度学习部署挑战的前沿研究成果。
14:00-14:40 (40m) 模型与方法 (Models and Methods)
非监督学习在大规模图谱上的案例应用和开源算法剖析
Mingxi Wu (TigerGraph)
图数据上的非监督学习在激活大数据的经济价值上有着广泛和不可替代的作用。 PageRank能够发掘重要的实体, 社区发掘(community detection)可以找到具有某种特性的群体,紧密度中心性算法(Closeness Centrality)可以自动找到远离群体的个体。所有这些算法都是非监督的学习。 我们分享一些具体客户案例来展示他们的价值,同时分享怎样在大数据上灵活应用这些开源算法。
14:50-15:30 (40m) 与人工智能交互 (Interacting with AI), 实施人工智能 (Implementing AI)
PAI tensor accelerator and optimizer: Yet another deep learning compiler
杨军 (阿里巴巴), 龙国平 (Alibaba)
本次演讲会介绍阿里计算平台PAI团队过去一年多时间里在深度学习编译器领域的技术工作进展----PAI TAO(Tensor Accelerator and Optimizer)。PAI-TAO采用通用编译优化技术,来解决PAI平台所承载的多样性AI workload面临的训练及推理需求的性能优化问题,在部分workload上获得了20%到4X不等的显著加速效果,并且基本作到用户层全透明,在显著提升平台效率性能的同时也有效照顾了用户的使用惯性。目前PAI-TAO已经先后用于支持阿里内部搜索、推荐、图像、文本等多个业务场景的日常训练及推理需求。
16:20-17:00 (40m) 实施人工智能 (Implementing AI)
Low-precision inference on Intel architecture
Lei Xia (Intel)
Vector neural network instructions (VNNI) is the new Intel instruction set for low-precision AI inference inside the next-generation Xeon platform. Lei Xia examines the features of the VNNI and Intel software tools so she can support developers as you use this new instruction set to accelerate inference with INT8.
08:00-08:30 (30m)
快速社交 (Speed Networking)
在本次人工智能大会上与寻求联系的与会者会面。会议将在周四主题演讲之前举行一个非正式的快速社交活动。一定要带上自己的名片来享受社交活动。
08:45-08:50 (5m)
Friday opening remarks
Ben Lorica (O'Reilly Media), Roger Chen (Computable), Jason (Jinquan) Dai (Intel)
Opening keynote remarks by program chairs Ben Lorica, Jason Dai, and Roger Chen.
08:50-09:00 (10m) 英文讲话 (Presented in English)
Unifying analytics and AI on big data for faster insights at scale
马子雅 (Ziya Ma) (Intel)
Keynote by Ziya Ma
09:00-09:15 (15m)
Keynote by Hao Zheng
Hao Zheng (PlusAI)
Keynote by Hao Zheng
09:15-09:30 (15m) 英文讲话 (Presented in English)
Keynote by Mikio Braun
Mikio Braun (Zalando SE)
Keynote by Mikio Braun
09:30-09:45 (15m)
Keynote with Yangqing Jia
Yangqing Jia (Alibaba Group)
Keynote with Yangqing Jia
09:45-09:55 (10m)
Keynote to come
Keynotes to come
09:55-10:15 (20m) 英文讲话 (Presented in English)
Keynote by Michael James
Michael James (Cerebras)
Keynote by Michael James
10:15-10:20 (5m)
Keynote to come
Keynotes to come
10:20-10:40 (20m) 英文讲话 (Presented in English)
Keynote by Tim Kraska
Tim Kraska (MIT)
Keynote by Tim Kraska
10:40-10:45 (5m)
Closing remarks
Ben Lorica (O'Reilly Media), Roger Chen (Computable)
Closing remarks with program chairs Ben Lorica and Roger Chen.
10:45-11:15 (30m)
Break: Morning Break
15:30-16:20 (50m)
Break: Afternoon Break
11:55-13:10 (1h 15m)
周五午餐主题桌会 (Friday Topic Tables at Lunch)
午餐时寻找和其他与会者的社交?主题桌会讨论帮助你结识相似行业或有共同话题的与会来宾。