Presented By
O’REILLY + INTEL AI

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

Tuesday, 2019-06-18

09:00

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09:00–17:00 Tuesday, 2019-06-18
2天培训 (2-day Training)
Location: 多功能厅6A+B (Function Room 6A+B)
Rich Ott (The Data Incubator)
PyTorch is a machine learning library for Python that allows users to build deep neural networks with great flexibility. Rich Ott introduces you to the PyTorch workflow and explores how its easy-to-use API and seamless use of GPUs makes it a sought-after tool for deep learning. He equips you with the knowledge you need to build deep learning models using real-world datasets. Read more.
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09:00–17:00 Tuesday, 2019-06-18
2天培训 (2-day Training)
与人工智能交互 (Interacting with AI)
Location: 多功能厅5A+B(Function Room 5A+B)
Season Yang (McKinsey & Company)
The TensorFlow library provides you with computational graphs with automatic parallelization across resources, ideal architecture for implementing neural networks. Season Yang introduces TensorFlow's capabilities in Python, and you'll get your hands dirty building machine learning algorithms piece by piece while using the Keras API provided by TensorFlow with several hands-on applications. Read more.
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09:00–17:00 Tuesday, 2019-06-18
2天培训 (2-day Training)
实施人工智能 (Implementing AI), 模型与方法 (Models and Methods)
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.

10:30

10:30–11:00 Tuesday, 2019-06-18
Location: TBD
Morning Break (30m)

12:30

12:30–13:30 Tuesday, 2019-06-18
Location: TBD
Lunch (1h)

15:00

15:00–15:30 Tuesday, 2019-06-18
Location: TBD
Afternoon Break (30m)

Wednesday, 2019-06-19

09:00

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09:00–12:30 Wednesday, 2019-06-19
Alejandro Saucedo (The Institute for Ethical AI & Machine Learning)
Undesired bias in machine learning has become a worrying topic after numerous high-profile incidents. Alejandro Saucedo uses a hands-on example to demystify machine learning bias. You'll automate the loan-approval process for a company and introduce key tools and techniques from the latest research that allows us to assess and mitigate undesired bias in machine learning models. Read more.
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09:00–12:30 Wednesday, 2019-06-19
3小时辅导课 (3-hour Tutorial)
中英文讲话(Presented in Chinese and English)
Location: 报告厅(Auditorium)
Yijing Chen (Microsoft), Dmitry Pechyoni (Microsoft), Angus Taylor (Microsoft), Vanja Paunic (Microsoft), Henry Zeng (Microsoft)
Almost every business today uses forecasting to make better decisions and allocate its resources more effectively. Deep learning has achieved a lot of success in computer vision, text, and speech processing, but has only recently been applied to time series forecasting. Presented in Chinese and English, you'll learn how and when to apply deep neural networks to time series forecasting. Read more.
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09:00–12:30 Wednesday, 2019-06-19
3小时辅导课 (3-hour Tutorial)
实施人工智能 (Implementing AI)
Location: 多功能厅8A+B(Function Room 8A+B)
Zhichao Li (Intel)
Zhichao Li shows 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) with real-world use cases (such as JD.com, MLSListings, World Bank, Baosight, Midea/KUKA, etc.) Read more.
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09:00–12:30 Wednesday, 2019-06-19
3小时辅导课 (3-hour Tutorial)
Location: 多功能厅5C(Function Room 5C)
Zhen Zhao (Intel)
Intel OpenVINO provides a highly optimized cross-platform deep learning deployment and visual AI solution based on various Intel architectures. Join Zhen Zhao as she explains the structure and workflow of the Intel OpenVINO toolkit, optimization methods by asynchronies, heterogeneous computing, low-precision inference, and instruction set acceleration. Read more.

10:30

10:30–11:00 Wednesday, 2019-06-19
Location: TBD
Morning Break (30m)

12:30

12:30–13:30 Wednesday, 2019-06-19
Location: TBD
Lunch (1h)

13:30

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13:30–17:00 Wednesday, 2019-06-19
3小时辅导课 (3-hour Tutorial)
人工智能对商业及社会的影响 (Impact of AI on Business and Society), 英文讲话 (Presented in English)
Location: 紫金大厅B(Grand Hall B)
Chris Butler (IPsoft)
Purpose, a well-defined problem, and trust are important factors to any system, especially those that employ AI. Chris Butler borrows from the principles of design thinking to lead you through exercises that help you create more impactful solutions and better align your team. Read more.
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13:30–17:00 Wednesday, 2019-06-19
3小时辅导课 (3-hour Tutorial)
实施人工智能 (Implementing AI), 模型与方法 (Models and Methods)
Location: 报告厅(Auditorium)
Lu Zhang (Microsoft), Henry Zeng (Microsoft), xiao zhang (Microsoft)
Intelligent experiences powered by AI seem like magic, but developing them is cumbersome, involving a series of sequential and interconnected decisions along the way that are time consuming. What if you had an automated service that identifies 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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13:30–17:00 Wednesday, 2019-06-19
3小时辅导课 (3-hour Tutorial)
英文讲话 (Presented in English)
Location: 多功能厅8A+B(Function Room 8A+B)
Richard Liaw (UC Berkeley RISELab)
Ray is a general-purpose framework for programming your cluster. Richard Liaw leads a deep dive into Ray, walking you through its API and system architecture and sharing application examples, including several state-of-the-art AI algorithms. Read more.

15:00

15:00–15:30 Wednesday, 2019-06-19
Location: TBD
Afternoon Break (30m)

Thursday, 2019-06-20

08:00

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08:00–08:30 Thursday, 2019-06-20
活动 (Event)
Location: 3楼序厅(3rd Floor Foyer)
本次人工智能会议上午8:00-8:30可以和希望社交的与会来宾见面。我们将在周五主题演讲之前搞一个非正式快速社交活动。一定记得带名片参加活动。 Read more.

08:45

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08:45–08:50 Thursday, 2019-06-20
主题演讲 (Keynote)
Location: 紫金大厅A(Grand Hall A)
Ben Lorica (O'Reilly Media), Jason (Jinquan) Dai (Intel), Roger Chen (Computable)
Opening keynote remarks by program chairs Ben Lorica, Jason Dai, and Roger Chen. Read more.

08:50

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08:50–09:05 Thursday, 2019-06-20
主题演讲 (Keynote)
Location: 紫金大厅A(Grand Hall A)
Abby Wen (Intel Corp.), Julie Shin Choi (Intel AI)
The Future of AI, a discussion with Abigail Wen and Julie Choi Read more.

09:05

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09:05–09:15 Thursday, 2019-06-20
主题演讲 (Keynote)
Location: 紫金大厅A(Grand Hall A)
Keynote by program chairs Ben Lorica and Roger Chen Read more.

09:15

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09:15–09:20 Thursday, 2019-06-20
主题演讲 (Keynote)
Location: 紫金大厅A(Grand Hall A)
Keynotes to come Read more.

09:20

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09:20–09:35 Thursday, 2019-06-20
主题演讲 (Keynote)
Location: 紫金大厅A(Grand Hall A)
Long Wang (Tencent)
Keynote by Long Wang, VP Tencent Cloud Read more.

09:35

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09:35–09:45 Thursday, 2019-06-20
主题演讲 (Keynote)
Location: 紫金大厅A(Grand Hall A)
Keynotes to come Read more.

09:45

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09:45–10:05 Thursday, 2019-06-20
主题演讲 (Keynote)
Location: 紫金大厅A(Grand Hall A)
Maria Zhang (LinkedIn)
Keynote by Maria Zhang Read more.

10:05

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10:05–10:20 Thursday, 2019-06-20
主题演讲 (Keynote)
Location: 紫金大厅A(Grand Hall A)
Pete Warden (Google)
On-device machine learning gives us the ability to turn this wasted data into actionable information, and will enable a massive number of new applications over the next few years. This talk will cover why embedded machine learning is so important, how it can be implemented on existing chips, and some of the new uses it will unlock. Read more.

10:20

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10:20–10:40 Thursday, 2019-06-20
主题演讲 (Keynote)
Location: 紫金大厅A(Grand Hall A)
Ion Stoica (UC Berkeley)
Keynote with Ion Stoica Read more.

10:40

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10:40–10:45 Thursday, 2019-06-20
主题演讲 (Keynote)
Location: 紫金大厅A(Grand Hall A)
Ben Lorica (O'Reilly Media), Roger Chen (Computable)
Closing remarks with Program Chairs Ben Lorica and Roger Chen Read more.

10:45

10:45–11:15 Thursday, 2019-06-20
Location: 报告厅序厅 (Auditorium Foyer)
Morning Break (30m)

11:15

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11:15–11:55 Thursday, 2019-06-20
40分钟议题 (40-minute session)
模型与方法 (Models and Methods), 英文讲话 (Presented in English)
Location: 紫金大厅B(Grand Hall B)
Tao Lu (Microsoft), Chenhui Hu (Microsoft)
Forecasting customer activities is an important, common business problem, and Tao Lu and Chenhui Hu forecast customer behavior based on billions of user activities. Join them as they share how Microsoft improves 25% of forecasting accuracy with dilated convolutional neural networks and reduces 80% of time in development with best practices of time series forecasting. Read more.
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11:15–11:55 Thursday, 2019-06-20
40分钟议题 (40-minute session)
实施人工智能 (Implementing AI)
Location: 报告厅(Auditorium)
刘先生 (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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11:15–11:55 Thursday, 2019-06-20
40分钟议题 (40-minute session)
模型与方法 (Models and Methods), 英文讲话 (Presented in English)
Location: 多功能厅2(Function Room 2)
David Low (Pand.ai)
Transfer learning has been a tremendous success in the computer vision field as a result of the ImageNet competition. In the past months, the natural language processing (NLP) field has witnessed several breakthroughs with transfer learning, namely ELMo, Transformer, ULMFit, and BERT. Join David Low as he showcases the use of transfer learning on NLP applications with state-of-the-art accuracy. Read more.
Add to your personal schedule
11:15–11:55 Thursday, 2019-06-20
40分钟议题 (40-minute session)
实施人工智能 (Implementing AI)
Location: 多功能厅5A+B(Function Room 5A+B)
Tags: wl
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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11:15–11:55 Thursday, 2019-06-20
40分钟议题 (40-minute session)
企业人工智能 (AI in the Enterprise)
Location: 多功能厅6A+B (Function Room 6A+B)
Min Shen (LinkedIn)
领英公司的几乎所有产品都离不开基于海量数据和大规模数据运算的机器学习模型。怎样构建一个稳定,高效,和易用的人工智能基础架构,越来越成为一个核心的问题。 这个演讲会先介绍领英大数据团队是怎样在5年的时间里演进这个基础架构,从开始的完全基于Spark的系统,到现在Spark+TensorFlow的环境。 我们还会重点介绍近期解决的技术挑战,来应对接近500PB数据和将近6亿会员的巨大经济图谱。这些挑战包括大规模重量级的深度学习模型,Spark的调优,以及在机器学习生产线中连接不同的步骤(数据准备,模型构建,模型训练,在线inference)。 最后我们会介绍我们近期一些成功的深度学习案例,以及团队在AI基础架构上未来2~3年的计划和愿景。 Read more.

11:55

Add to your personal schedule
11:55–13:10 Thursday, 2019-06-20
活动 (Event)
Location: 彩虹厅及国际厅 (Rainbow Room & Ballroom)
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。 Read more.

13:10

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13:10–13:50 Thursday, 2019-06-20
40分钟议题 (40-minute session)
模型与方法 (Models and Methods), 英文讲话 (Presented in English)
Location: 紫金大厅B(Grand Hall B)
Bichen Wu (UC Berkeley)
The success of deep neural networks is attributed to three factors: stronger computing capacity, more complex neural networks, and more data. These factors, however, are usually not available with the edge applications as autonomous driving, AR/VR, IoT, and so on. Bichen Wu explains how you can apply AutoML, SW/HW codesign, and domain adaptation to solve these problems. Read more.
Add to your personal schedule
13:10–13:50 Thursday, 2019-06-20
40分钟议题 (40-minute session)
实施人工智能 (Implementing AI), 模型与方法 (Models and Methods)
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. The session will be delivered in English and Chinese jointly. Read more.
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13:10–13:50 Thursday, 2019-06-20
40分钟议题 (40-minute session)
英文讲话 (Presented in English), 隐私、道德与规范 (Privacy, Ethics, and Compliance)
Location: 多功能厅2(Function Room 2)
Alex Ingerman (Google)
Federated learning is the approach of training ML models across a fleet of participating devices without collecting their data in a central location. Join Alex Ingerman as he examines federated learning, compares the traditional and federated ML workflows, and explores the current and upcoming use cases for decentralized machine learning with examples from Google's deployment of this technology. Read more.
Add to your personal schedule
13:10–13:50 Thursday, 2019-06-20
40分钟议题 (40-minute session)
实施人工智能 (Implementing AI)
Location: 多功能厅5A+B(Function Room 5A+B)
Ying Liu (Abakus 鲸算科技(Wecash闪银))
The AI debt collection platform of Abakus provides a friendly and humane product solution designed for people who work as the frontline, live agents of the organization. The company's agent training could be enhanced with an AI-friendly culture. Join Ying Liu as she details the results of an experiment showing the performance of the collection assistants has been highly improved. Read more.
Add to your personal schedule
13:10–13:50 Thursday, 2019-06-20
40分钟议题 (40-minute session)
与人工智能交互 (Interacting with AI), 模型与方法 (Models and Methods)
Location: 多功能厅6A+B (Function Room 6A+B)
姜涛 (Kwai)
介绍如何综合应用多项人工智能技术进行K歌修音和短视频自动配乐,涉及的相关技术包括:人声/音乐分离、高精度的基频提取、自动作曲/作词技术、基于视频内容的音乐生成等。 Read more.

14:00

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14:00–14:40 Thursday, 2019-06-20
40分钟议题 (40-minute session)
Location: 紫金大厅B(Grand Hall B)
Pete Warden (Google)
Pete Warden explores how you can use Google's open source framework to run machine learning models on embedded processors like microcontrollers and DSPs. Discover what you need to get started using the code itself, including hardware, coding tools, and getting the library built. Read more.
Add to your personal schedule
14:00–14:40 Thursday, 2019-06-20
40分钟议题 (40-minute session)
实施人工智能 (Implementing AI)
Location: 报告厅(Auditorium)
Tiezhen Wang (Google)
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. He explores distributed strategies and edge deployment (TensorFlow Lite and TensorFlow.js). Read more.
Add to your personal schedule
14:00–14:40 Thursday, 2019-06-20
40分钟议题 (40-minute session)
模型与方法 (Models and Methods), 英文讲话 (Presented in English)
Location: 多功能厅2(Function Room 2)
Arun Verma (Bloomberg)
Join Arun Verma as he examines the use of AI and machine learning (ML) techniques in quantitative finance that lead to profitable trading strategies. Passive investing (quantamental investing) is popular, and many techniques from deep learning and reinforcement learning as well as NLP and sentiment analysis are being used for a broad set of datasets such as news and geolocational data. Read more.
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14:00–14:40 Thursday, 2019-06-20
40分钟议题 (40-minute session)
执行简报/最佳实践 (Executive Briefing/Best Practices)
Location: 多功能厅5A+B(Function Room 5A+B)
杨博理 (宜信大数据创新中心)
AI技术是线上财富管理领域中不可或缺的一环。在这个演讲中,我会将财富管理进一步细分为投资和实现财务目标两个方面,并分别讲解AI技术在这两个细分层面上的应用问题。对于投资而言,一些具备强金融逻辑的变量可能更适合使用机器学习进行预测。而在资产价格的预测上,可以尝试使用AI和大数据技术获取更多的有价值信息。对于实现财务目标而言,基于NLP技术的语义理解、引导式对话是理解用户的关键,基于AI和大数据的KYC也是判断用户状态的有效工具,而一个融合了财务规划、投资和精算知识的专家系统则是定制级规划的核心。 Read more.
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14:00–14:40 Thursday, 2019-06-20
40分钟议题 (40-minute session)
模型与方法 (Models and Methods)
Location: 多功能厅6A+B (Function Room 6A+B)
peng ni (凡普金科集团有限公司)
该议题主要包括:1. 语音切分技术的原理和应用;2. 语音识别模型的构建优化;3. 语音情感分析构建应用;4. 语音数据的实时处理框架;5. 金融场景业务落地。 Read more.

14:50

Add to your personal schedule
14:50–15:30 Thursday, 2019-06-20
40分钟议题 (40-minute session)
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)
Location: 紫金大厅B(Grand Hall B)
Mark Ryan (IBM), Alina Li Zhang (Skylinerunners)
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.
Add to your personal schedule
14:50–15:30 Thursday, 2019-06-20
40分钟议题 (40-minute session)
实施人工智能 (Implementing AI), 模型与方法 (Models and Methods)
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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14:50–15:30 Thursday, 2019-06-20
40分钟议题 (40-minute session)
实施人工智能 (Implementing AI), 英文讲话 (Presented in English)
Location: 多功能厅2(Function Room 2)
Chenta Lee (IBM)
By combining various analytics including DGA, squatting, tunneling, and rebinding detection, we built a DNS analytic playbook to anneal actionable threat intelligence from billions of DNS requests. We will show how DNS volumetric data and analytics complement each other to create an new dimension to look at security postures. Moreover, how to leverage it in security operations? Read more.
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14:50–15:30 Thursday, 2019-06-20
40分钟议题 (40-minute session)
实施人工智能 (Implementing AI), 模型与方法 (Models and Methods)
Location: 多功能厅5A+B(Function Room 5A+B)
Guoqiong Song (Intel), Luyang Wang (Office Depot), Jiao(Jennie) Wang (Intel), Jing (Nicole) Kong (Office Depot)
Real-time recommender systems are critical for the success of the ecommerce industry. Join Guoqiong Song, Luyang Wang, Jiao Wang, and Jing Kong as they showcase how to build efficient recommender systems for the ecommerce industry using deep learning technologies. Read more.
14:50–15:30 Thursday, 2019-06-20
40分钟议题 (40-minute session)
模型与方法 (Models and Methods)
Location: 多功能厅6A+B (Function Room 6A+B)
TBC

15:30

15:30–16:20 Thursday, 2019-06-20
Location: 报告厅序厅 (Auditorium Foyer)
Afternoon Break (50m)

16:20

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16:20–17:00 Thursday, 2019-06-20
40分钟议题 (40-minute session)
人工智能对商业及社会的影响 (Impact of AI on Business and Society), 英文讲话 (Presented in English)
Location: 紫金大厅B(Grand Hall B)
David Maman (Binah)
Zero-day attacks. IoT-based botnets. Cybercriminal AI versus cyberdefender AI. While these won’t be going away, they aren’t our biggest worry in cybercrime. Hacking humans is. David Maman explores how the combination of minutes of video, signal processing, remote heart-rate monitoring, AI, ML, and data science can identify a person’s health vulnerabilities, which evildoers can make worse. Read more.
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16:20–17:00 Thursday, 2019-06-20
40分钟议题 (40-minute session)
人工智能对商业及社会的影响 (Impact of AI on Business and Society)
Location: 报告厅(Auditorium)
温浩 (云从科技)
AI企业发展应该是一个从学术研究、行业验证、商业落地、行业平台到智能生态的一层层深入过程,这也是人工智能企业理想的发展阶段。 云从科技计划打造核心技术闭环,让计算机更好地服务人类。并将全面降低人工智能准入门槛,让“AI普惠”成为可能。 Read more.
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16:20–17:00 Thursday, 2019-06-20
40分钟议题 (40-minute session)
实施人工智能 (Implementing AI), 英文讲话 (Presented in English)
Location: 多功能厅2(Function Room 2)
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. Shengsheng Huang takes you through her experiences from her attempts in using the AI on Spark for playing games. Read more.
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16:20–17:00 Thursday, 2019-06-20
40分钟议题 (40-minute session)
实施人工智能 (Implementing AI)
Location: 多功能厅5A+B(Function Room 5A+B)
Dongfeng Chen (Clobotics)
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, so you don't have to. Read more.
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16:20–17:00 Thursday, 2019-06-20
40分钟议题 (40-minute session)
实施人工智能 (Implementing AI)
Location: 多功能厅6A+B (Function Room 6A+B)
Derek Haoyang Li (松鼠AI Squirrel AI Learning)
Squirrel AI Learning is the first artificial intelligence technology company in China to apply AI-adaptive technology to K–12 education. Derek Haoyang Li 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.

17:00

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17:00–17:45 Thursday, 2019-06-20
活动 (Event)
Location: 赞助商区域 (Sponsor Pavilion)
Come enjoy snacks and beverages with fellow AI Conference attendees, speakers, and sponsors. Read more.

Friday, 2019-06-21

08:00

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08:00–08:30 Friday, 2019-06-21
活动 (Event)
Location: 3楼序厅(3rd Floor Foyer)
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。 Read more.

08:45

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08:45–08:50 Friday, 2019-06-21
主题演讲 (Keynote)
Location: 紫金大厅A(Grand Hall A)
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. Read more.

08:50

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08:50–09:00 Friday, 2019-06-21
主题演讲 (Keynote)
Location: 紫金大厅A(Grand Hall A)
Keynote by Ziya Ma Read more.

09:00

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09:00–09:15 Friday, 2019-06-21
主题演讲 (Keynote)
Location: 紫金大厅A(Grand Hall A)
Hao Zheng (PlusAI)
Keynote by Hao Zheng Read more.

09:15

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09:15–09:30 Friday, 2019-06-21
主题演讲 (Keynote)
Location: 紫金大厅A(Grand Hall A)
Mikio Braun (Zalando SE)
Keynote by Mikio Braun Read more.

09:30

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09:30–09:45 Friday, 2019-06-21
主题演讲 (Keynote)
Location: 紫金大厅A(Grand Hall A)
Yangqing Jia (Alibaba Group)
Keynote with Yangqing Jia Read more.

09:45

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09:45–09:55 Friday, 2019-06-21
主题演讲 (Keynote)
Location: 紫金大厅A(Grand Hall A)
Keynotes to come Read more.

09:55

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09:55–10:15 Friday, 2019-06-21
主题演讲 (Keynote)
Location: 紫金大厅A(Grand Hall A)
Michael James (Cerebras)
Keynote by Michael James Read more.

10:15

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10:15–10:20 Friday, 2019-06-21
主题演讲 (Keynote)
Location: 紫金大厅A(Grand Hall A)
Keynotes to come Read more.

10:20

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10:20–10:40 Friday, 2019-06-21
主题演讲 (Keynote)
Location: 紫金大厅A(Grand Hall A)
Tim Kraska (MIT)
Keynote by Tim Kraska Read more.

10:40

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10:40–10:45 Friday, 2019-06-21
主题演讲 (Keynote)
Location: 紫金大厅A(Grand Hall A)
Ben Lorica (O'Reilly Media), Roger Chen (Computable)
Closing remarks with program chairs Ben Lorica and Roger Chen. Read more.

10:45

10:45–11:15 Friday, 2019-06-21
Location: 报告厅序厅 (Auditorium Foyer)
Morning Break (30m)

11:15

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11:15–11:55 Friday, 2019-06-21
40分钟议题 (40-minute session)
英文讲话 (Presented in English)
Location: 紫金大厅B(Grand Hall B)
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. Read more.
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11:15–11:55 Friday, 2019-06-21
40分钟议题 (40-minute session)
实施人工智能 (Implementing AI), 英文讲话 (Presented in English)
Location: 报告厅(Auditorium)
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. Read more.
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11:15–11:55 Friday, 2019-06-21
40分钟议题 (40-minute session)
执行简报/最佳实践 (Executive Briefing/Best Practices), 英文讲话 (Presented in English)
Location: 多功能厅2(Function Room 2)
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. Read more.
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11:15–11:55 Friday, 2019-06-21
40分钟议题 (40-minute session)
实施人工智能 (Implementing AI)
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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11:15–11:55 Friday, 2019-06-21
40分钟议题 (40-minute session)
实施人工智能 (Implementing AI)
Location: 多功能厅6A+B (Function Room 6A+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 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. Read more.

11:55

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11:55–13:10 Friday, 2019-06-21
活动 (Event)
Location: 彩虹厅及国际厅 (Rainbow Room & Ballroom)
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。 Read more.

13:10

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13:10–13:50 Friday, 2019-06-21
40分钟议题 (40-minute session)
人工智能对商业及社会的影响 (Impact of AI on Business and Society)
Location: 紫金大厅B(Grand Hall B)
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. Read more.
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13:10–13:50 Friday, 2019-06-21
40分钟议题 (40-minute session)
实施人工智能 (Implementing AI)
Location: 报告厅(Auditorium)
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。 Read more.
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13:10–13:50 Friday, 2019-06-21
40分钟议题 (40-minute session)
实施人工智能 (Implementing AI), 英文讲话 (Presented in English)
Location: 多功能厅2(Function Room 2)
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. Read more.
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13:10–13:50 Friday, 2019-06-21
40分钟议题 (40-minute session)
案例研究 (Case Studies)
Location: 多功能厅5A+B(Function Room 5A+B)
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. Read more.
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13:10–13:50 Friday, 2019-06-21
40分钟议题 (40-minute session)
模型与方法 (Models and Methods)
Location: 多功能厅6A+B (Function Room 6A+B)
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。 Read more.

14:00

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14:00–14:40 Friday, 2019-06-21
40分钟议题 (40-minute session)
模型与方法 (Models and Methods)
Location: 紫金大厅B(Grand Hall B)
Tags: wl
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. Read more.
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14:00–14:40 Friday, 2019-06-21
40分钟议题 (40-minute session)
实施人工智能 (Implementing AI)
Location: 报告厅(Auditorium)
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. Read more.
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14:00–14:40 Friday, 2019-06-21
40分钟议题 (40-minute session)
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)
Location: 多功能厅2(Function Room 2)
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. Read more.
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14:00–14:40 Friday, 2019-06-21
40分钟议题 (40-minute session)
与人工智能交互 (Interacting with AI), 实施人工智能 (Implementing AI)
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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14:00–14:40 Friday, 2019-06-21
40分钟议题 (40-minute session)
模型与方法 (Models and Methods)
Location: 多功能厅6A+B (Function Room 6A+B)
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。 Read more.

14:50

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14:50–15:30 Friday, 2019-06-21
40分钟议题 (40-minute session)
实施人工智能 (Implementing AI), 模型与方法 (Models and Methods), 英文讲话 (Presented in English)
Location: 紫金大厅B(Grand Hall B)
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. Read more.
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14:50–15:30 Friday, 2019-06-21
40分钟议题 (40-minute session)
实施人工智能 (Implementing AI)
Location: 报告厅(Auditorium)
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. Read more.
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14:50–15:30 Friday, 2019-06-21
40分钟议题 (40-minute session)
企业人工智能 (AI in the Enterprise), 实施人工智能 (Implementing AI)
Location: 多功能厅2(Function Room 2)
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. Read more.
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14:50–15:30 Friday, 2019-06-21
40分钟议题 (40-minute session)
人工智能对商业及社会的影响 (Impact of AI on Business and Society)
Location: 多功能厅5A+B(Function Room 5A+B)
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。 Read more.
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14:50–15:30 Friday, 2019-06-21
40分钟议题 (40-minute session)
与人工智能交互 (Interacting with AI), 实施人工智能 (Implementing AI)
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.

15:30

15:30–16:20 Friday, 2019-06-21
Location: 报告厅序厅 (Auditorium Foyer)
Afternoon Break (50m)

16:20

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16:20–17:00 Friday, 2019-06-21
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. Read more.
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16:20–17:00 Friday, 2019-06-21
40分钟议题 (40-minute session)
实施人工智能 (Implementing AI)
Location: 报告厅(Auditorium)
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。 Read more.
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16:20–17:00 Friday, 2019-06-21
40分钟议题 (40-minute session)
模型与方法 (Models and Methods), 英文讲话 (Presented in English)
Location: 多功能厅2(Function Room 2)
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. Read more.
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16:20–17:00 Friday, 2019-06-21
40分钟议题 (40-minute session)
实施人工智能 (Implementing AI)
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)
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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16:20–17:00 Friday, 2019-06-21
40分钟议题 (40-minute session)
实施人工智能 (Implementing AI)
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 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. Read more.