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PUT AI TO WORK
June 18-21, 2019
Beijing, CN

Schedule: 实施人工智能 (Implementing AI) sessions

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09:00 - 17:00 Tuesday, June 18 & Wednesday, June 19
Location: 多功能厅2(Function Room 2)
Jike Chong (Tsinghua University | Acorns), 黄铃 (Tsinghua University), 陈薇 (排列科技)
您想了解金融企业是怎样利用大数据和人工智能技术来画像个人行为并检测欺诈用户的吗?互联网金融幕后的量化分析流程是怎么杨的?个人信用是怎样通过大数据被量化的?在实践过程中,机器学习算法的应用存在着哪些需要关注的方面?怎样通过图谱分析来融合多维数据,为我们区分正常用户和欺诈用户? 这套辅导课基于清华大学交叉信息研究院开设的一门"量化金融信用与风控分析”研究生课。其中会用LendingClub的真实借贷数据做为案例,解说一些具体模型的实现。 Read more.
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09:0012:30 Wednesday, June 19, 2019
Location: 紫金大厅B(Grand Hall B)
Alejandro Saucedo (The Institute for Ethical AI & Machine Learning)
Numerous high-profile incidents have proved undesired bias in machine learning a worrying topic. Alejandro Saucedo uses a hands-on example to demystify machine learning bias. You'll automate the loan-approval process for a company and explore key tools and techniques from the latest research that allows you to assess and mitigate undesired bias in machine learning models. Read more.
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09:0012:30 Wednesday, June 19, 2019
Location: 多功能厅5C(Function Room 5C)
Zhichao Li (Intel), Kai Huang (Intel), Yang Wang (Intel)
Zhichao Li, Kai Huang, and Yang Wang show you how to build and productionize deep learning applications for big data using Analytics Zoo—a unified analytics and AI platform that seamlessly unites Spark, TensorFlow, Keras, and BigDL programs into an integrated pipeline—illustrated though real-world use cases from JD.com, MLSListings, the World Bank, Baosight, and Midea/KUKA. Read more.
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13:3017:00 Wednesday, June 19, 2019
Location: 报告厅(Auditorium)
Henry Zeng (Microsoft), Lu Zhang (Microsoft), xiao zhang (Microsoft)
Average rating: ****.
(4.00, 3 ratings)
Intelligent experiences powered by AI seem like magic, but developing them is cumbersome, involving a series of time consuming sequential and interconnected decisions along the way. What if you had an automated service that could identify the best machine learning pipelines for your given problem or data? Lu Zhang, Henry Zeng, and Xiao Zhang detail how automated machine learning does that. Read more.
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11:1511:55 Thursday, June 20, 2019
Location: 报告厅(Auditorium)
刘怀军 (美团)
该议题的内容包括: 1.外卖个性化场景:个性化搜索,个性化推荐 2.个性化产品形态包括:商家、商品、套餐等 3.外卖个性化中应用的AI技术包括:NLP,DNN,图像技术,强化学习 4.针对外卖业务的特点,介绍个性化场景中,几项重点AI技术的落地、挑战与思考 Read more.
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11:1511:55 Thursday, June 20, 2019
Location: 多功能厅5A+B(Function Room 5A+B)
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 explains why gaming is a great scenario for AI and walks you through recent research in the future of AI games involving reinforcement learning, natural language processing (NLP), computer vision and graphics, and user personas and virtual humans. Read more.
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13:1013:50 Thursday, June 20, 2019
Location: 报告厅(Auditorium)
Henry Zeng (Microsoft), Klein Hu (Microsoft), Emma Ning (Microsoft)
An open and interoperable ecosystem enables you to choose the framework that's right for you, train at scale, and deploy to cloud and edge. ONNX provides a common format supported by many popular frameworks and hardware accelerators. Henry Zeng, Klein Hu, and Emma Ning introduce you to ONNX and its core concepts. (Presented in English and Chinese.) Read more.
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13:1013:50 Thursday, June 20, 2019
Location: 多功能厅5A+B(Function Room 5A+B)
Ying Liu (Abakus 鲸算科技(Wecash闪银))
Abakus's AI debt collection platform provides a friendly and humane product solution designed for people who work on the frontline: live agents of the organization. The company's agent training has been enhanced with an AI-friendly culture. Join Ying Liu as she details the results of an experiment showing how the company improved the performance of the collection assistants. Read more.
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14:0014:40 Thursday, June 20, 2019
Location: 报告厅(Auditorium)
Hui Xue (微软亚洲研究院)
人工智能在过去的几年里飞速发展,但是机器学习的实践和应用需要消耗一定的人力和时间。例如,如何去做特征选择,如何设计一个适合该任务的神经网络模型等等。而自动机器学习技术,可以帮助开发者和机器学习实战者,缩短开发周期,提高效率。我们的介绍主要包括:自动机器学习技术的进展;我们开源的自动机器学习开源库neural network intelligence; 如何利用自动机器学习的技术,在产品和应用上提高效率,节省所需的时间和缩短周期。我们会在最后一部分,分享一些利用自动特征选择,自动参数调整以及模型架构搜索上的成功案例。 Read more.
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14:5015:30 Thursday, June 20, 2019
Location: 紫金大厅B(Grand Hall B)
Mark Ryan (IBM), Alina Zhang (Skylinerunners)
Average rating: *****
(5.00, 1 rating)
Toronto is unique among North American cities for having a legacy streetcar network as an integral part of its transit system. This means streetcar delays are a major contributor to gridlock in the city. Learn about applying deep learning time series forecasting to machine learning as Mark Ryan and Alina Li Zhang explain how streetcar delays can be predicted...and prevented. Read more.
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14:5015:30 Thursday, June 20, 2019
Location: 报告厅(Auditorium)
Tiezhen Wang (Google)
Average rating: *****
(5.00, 2 ratings)
TensorFlow 2.0 is a major milestone with a focus on ease of use. Tiezhen Wang walks you through the new exciting features and best practices. Join in to explore distributed strategies and edge deployment (TensorFlow Lite and TensorFlow.js). Read more.
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14:5015:30 Thursday, June 20, 2019
Location: 多功能厅2(Function Room 2)
Chenta Lee (IBM)
By combining various analytics including DGA, squatting, tunneling, and rebinding detection, it's possible to build a DNS analytic playbook to anneal actionable threat intelligence from billions of DNS requests. Chenta Lee outlines how DNS volumetric data and analytics complement each other to create a new dimension to look at security postures and how to leverage it in security operations. Read more.
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14:5015:30 Thursday, June 20, 2019
Location: 多功能厅5A+B(Function Room 5A+B)
Kai Huang (Intel)
Real-time recommender systems are critical for the success of the ecommerce industry. Join Kai Huang, Luyang Wang, and Jing Kong as they showcase how to build efficient recommender systems for the ecommerce industry using deep learning technologies. Read more.
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16:2017:00 Thursday, June 20, 2019
Location: 多功能厅5A+B(Function Room 5A+B)
Dongfeng Chen (Clobotics)
Average rating: ****.
(4.00, 1 rating)
One of the biggest challenges to growth remains the high costs of constructing wind farms, as well as the ongoing operations and maintenance costs. Dongfeng Chen dives into the successes and failures of creating an entirely autonomous visual-recognition-powered drone inspection solution for turbine blades, which increased the efficiency by 10 times. Read more.
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16:2017:00 Thursday, June 20, 2019
Location: 多功能厅6A+B (Function Room 6A+B)
Xing Fan (Squirrel AI)
Squirrel AI Learning is the first artificial intelligence technology company in China to apply AI-adaptive technology to K–12 education. Xing Fan dives deep into its implementation approach and teaches you about the business process, pedagogy, architecture, operation, and theoretical underpinning of this adaptive learning service. Read more.
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16:2017:00 Thursday, June 20, 2019
Location: 多功能厅8A+B(Function Room 8A+B)
Shan Yu (Intel)
Average rating: ****.
(4.00, 1 rating)
Using AI to play games is often perceived as an early step toward achieving general machine intelligence, as the ability to reason and make decisions based on sensed information is an essential part of general intelligence. Shan Yu shares lessons learned from her attempts using AI on Spark to play games. Read more.
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11:1511:55 Friday, June 21, 2019
Location: 报告厅(Auditorium)
Yang Wang (Intel)
Building a model is fun and exciting; putting it to production is a different story. Yang Wang offers an overview of Analytics Zoo, a unified analytics and AI platform for distributed TensorFlow, Keras, and BigDL on Apache Spark, designed for production environments. See how you can benefit from its easy deployment, high performance, and efficient model serving for deep learning applications. Read more.
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11:1511:55 Friday, June 21, 2019
Location: 多功能厅5A+B(Function Room 5A+B)
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。 Read more.
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11:1511:55 Friday, June 21, 2019
Location: 多功能厅6A+B (Function Room 6A+B)
刘祁跃 (爱奇艺)
对视频进行精彩度分析,有助于筛选优质内容,尤其是冷启动阶段 同时,基于算法对精彩内容的理解,可以辅助创作,如进行标题辅助生成、动态/精彩封面生成、智能拆条等 我们通过对视频、音频、文本等多模态内容分析,同时利用用户交互数据,建立了完备的视频精彩度分析系统,并落地在长/短视频的不同业务场景下,明显提升了业务产出质量和效率 Read more.
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13:1013:50 Friday, June 21, 2019
Location: 报告厅(Auditorium)
Yiheng Wang (Tencent)
Average rating: *****
(5.00, 1 rating)
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。 Read more.
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13:1013:50 Friday, June 21, 2019
Location: 多功能厅2(Function Room 2)
Mikio Braun (Zalando)
Mikio Braun takes you through the past 20 years of machine learning research to explore aspects of artificial intelligence, then examines current examples like autonomous cars and chatbots. Together you'll put together a mental model for a reference architecture for artificial intelligence systems. Read more.
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14:0014:40 Friday, June 21, 2019
Location: 紫金大厅B(Grand Hall B)
Kaz Sato (Google)
Average rating: *****
(5.00, 1 rating)
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. Read more.
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14:0014:40 Friday, June 21, 2019
Location: 报告厅(Auditorium)
Chaoguang Li (Qiniu), Bin Fan (Alluxio), Haoyuan Li (Alluxio)
The Atlab Lab at Qiniu Cloud focuses on deep learning for computer vision. Chaoguang Li, Haoyuan Li, and Bin Fan lead a deep dive into AVA, a high-performance and cost-effective cloud-based training platform for deep learning, which deeply integrates an open source software stack including TensorFlow, Caffe, Alluxio, and KODO, the company's own cloud object storage. Read more.
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14:0014:40 Friday, June 21, 2019
Location: 多功能厅2(Function Room 2)
Orchlon Ann (Rakuten), TzuLin Chin (Rakuten)
Average rating: ****.
(4.00, 1 rating)
Orchlon Ann and TzuLin Chin offer an overview of 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 it's benefits, including one-click machine learning environment creation, a powerful job scheduler, and a flexible function-as-a-service component. Read more.
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14:0014:40 Friday, June 21, 2019
Location: 多功能厅5A+B(Function Room 5A+B)
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。 Read more.
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14:5015:30 Friday, June 21, 2019
Location: 报告厅(Auditorium)
Zhenxiao Luo (Twitter)
Average rating: *****
(5.00, 2 ratings)
Locations and trips provide insights that can improve business decisions and better serve users, but geospatial data analysis is particularly challenging. It requires efficiency, usability, and scalability in order to meet user needs and business requirements. Join Zhenxiao Luo to learn how Uber uses artificial intelligence to analyze geospatial big data, one of its distinct challenges. Read more.
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14:5015:30 Friday, June 21, 2019
Location: 多功能厅2(Function Room 2)
AI pipelines simplify the lifecycle workflow management and enhance reproducibility and collaboration for machine learning and deep learning projects. Cloud native platform solutions offer great portability and scalability. Weiqiang Zhuang and Huaxin Gao show how, by combining strengths, AI pipelines on container platforms can help accelerate AI application development and deployment. Read more.
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14:5015:30 Friday, June 21, 2019
Location: 多功能厅6A+B (Function Room 6A+B)
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。 Read more.
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16:2017:00 Friday, June 21, 2019
Location: 报告厅(Auditorium)
ju fang (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。 Read more.
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16:2017:00 Friday, June 21, 2019
Location: 多功能厅2(Function Room 2)
Le Zhang (Microsoft), Jianxun Lian (Microsoft)
Enterprises benefit from recommendation systems for revenue and customer engagement, but creating such a system is time-consuming. Le Zhang and Jianxun Lian explore the Microsoft/Recommenders repository, which offers solutions to building recommendation systems. It contains classic and state-of-the-art algorithms from Microsoft and enables enterprise success by leveraging Azure's cloud capability. Read more.
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16:2017:00 Friday, June 21, 2019
Location: 多功能厅5A+B(Function Room 5A+B)
李苍柏 (中国地质科学院矿产资源研究所)
Average rating: *....
(1.00, 1 rating)
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。 Read more.
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16:2017:00 Friday, June 21, 2019
Location: 多功能厅6A+B (Function Room 6A+B)
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 offers an overview of the VNNI and Intel software tools, helping you use this new instruction set to accelerate inference with INT8. Read more.