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 TensorFlow lite for microcontrollers to your personal schedule
14:00 TensorFlow lite for microcontrollers Pete Warden (Google)
Add Using deep learning and time series forecasting to reduce transit delays to your personal schedule
14:50 Using deep learning and time series forecasting to reduce transit delays Mark Ryan (IBM), Alina Zhang (Skylinerunners)
报告厅(Auditorium)
Add ONNX:开放和互操作平台让AI无处不在(AI everywhere: Open and interoperable platform for AI with ONNX) to your personal schedule
13:10 ONNX:开放和互操作平台让AI无处不在(AI everywhere: Open and interoperable platform for AI with ONNX) Henry Zeng (Microsoft), Klein Hu (Microsoft), Emma Ning (Microsoft)
Add 打造A.I.闭环   引领产业变革 to your personal schedule
16:20 打造A.I.闭环 引领产业变革 温浩 (云从科技)
多功能厅2(Function Room 2)
Add Detect the undetectable at the breach to your personal schedule
14:50 Detect the undetectable at the breach Chenta Lee (IBM)
Add Using ML for personalizing food recommendations to your personal schedule
16:20 Using ML for personalizing food recommendations Maulik Soneji (GO-JEK), Jewel James (Gojek)
多功能厅5A+B(Function Room 5A+B)
Add 线上财富管理领域中的AI应用 to your personal schedule
14:00 线上财富管理领域中的AI应用 杨博理 (宜信大数据创新中心)
多功能厅6A+B (Function Room 6A+B)
Add 深度学习语音技术在金融场景中的应用 to your personal schedule
14:00 深度学习语音技术在金融场景中的应用 peng ni (凡普金科集团有限公司)
多功能厅8A+B(Function Room 8A+B)
14:50
08:00 Morning Coffee | Room: 3楼序厅(3rd Floor Foyer)
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 Thursday opening remarks to your personal schedule
紫金大厅A(Grand Hall A)
08:45 Thursday opening remarks Ben Lorica (O'Reilly Media), Jason (Jinquan) Dai (Intel), Roger Chen (Computable)
Add Accelerating AI adoption to your personal schedule
09:05 Accelerating AI adoption Ben Lorica (O'Reilly Media), Roger Chen (Computable)
Add The future of machine learning is tiny to your personal schedule
10:05 The future of machine learning is tiny Pete Warden (Google)
Add AI and systems at RISELab to your personal schedule
10:20 AI and systems at RISELab Ion Stoica (University of California, Berkeley)
Add Closing remarks to your personal schedule
10:40 Closing remarks Ben Lorica (O'Reilly Media), Jason (Jinquan) Dai (Intel), Roger Chen (Computable)
10:45 Morning Break - Sponsored by Dell Technologies | Room: 报告厅序厅 (Auditorium Foyer)
15:30 Afternoon Break | Room: 报告厅序厅 (Auditorium Foyer)
Add 周四午餐主题桌会 (Thursday Topic Tables at Lunch) to your personal schedule
11:55 Lunch - Sponsored by Habana Labs 周四午餐主题桌会 (Thursday Topic Tables at Lunch) | Room: 彩虹厅及国际厅 (Rainbow Room & Ballroom)
Add 与会来宾欢乐时光(Attendee Happy Hour) to your personal schedule
17:00 与会来宾欢乐时光(Attendee Happy Hour) | Room: 赞助商区域 (Sponsor Pavilion)
11:15-11:55 (40m) 模型与方法 (Models and Methods), 英文讲话 (Presented in English)
Forecasting customer activities with dilated convolution neural networks: Use case and best practices
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 improved forecasting accuracy by 25% with dilated convolutional neural networks and reduced time in development by 80% with a set of time series forecasting best practices.
13:10-13:50 (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)
ING is a data-driven enterprise, with analytics skills as a top strategic priority. AI is at the core of ING’s business, and the company 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 company's use cases and technology.
14:00-14:40 (40m) 英文讲话 (Presented in English)
TensorFlow lite for microcontrollers
Pete Warden (Google)
Pete Warden explains how to 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.
14:50-15:30 (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)
Using deep learning and time series forecasting to reduce transit delays
Mark Ryan (IBM), Alina 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.
16:20-17:00 (40m) 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)
Hacking humans made easy: Signal processing + AI + video
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 demonstrates 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.
11:15-11:55 (40m) 实施人工智能 (Implementing AI)
AI技术在外卖个性化场景中的落地与思考
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
13:10-13:50 (40m) 实施人工智能 (Implementing AI), 模型与方法 (Models and Methods)
ONNX:开放和互操作平台让AI无处不在(AI everywhere: Open and interoperable platform for AI with ONNX)
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.)
14:00-14:40 (40m) 实施人工智能 (Implementing AI), 模型与方法 (Models and Methods)
自动机器学习(Automated machine learning)技术的实践与应用
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。
14:50-15:30 (40m) 实施人工智能 (Implementing AI)
令人兴奋的TensorFlow 2.0新功能(Exciting new features in TensorFlow 2.0)
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. Join in to explore distributed strategies and edge deployment (TensorFlow Lite and TensorFlow.js).
16:20-17:00 (40m) 人工智能对商业及社会的影响 (Impact of AI on Business and Society)
打造A.I.闭环 引领产业变革
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。
11:15-11:55 (40m) 模型与方法 (Models and Methods), 英文讲话 (Presented in English)
The unreasonable effectiveness of transfer learning on natural language processing
David Low (Pand.ai)
Transfer learning has been a tremendous success in computer vision as a result of the ImageNet competition. In the past few months, natural language processing (NLP) 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.
13:10-13:50 (40m) 英文讲话 (Presented in English), 隐私、道德与规范 (Privacy, Ethics, and Compliance)
The future of machine learning is decentralized
Alex Ingerman (Google)
Federated learning involves training ML models across a fleet of participating devices without collecting their data in a central location. Alex Ingerman 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.
14:00-14:40 (40m) 模型与方法 (Models and Methods), 英文讲话 (Presented in English)
Trading strategies using alternative data and machine learning
Arun Verma (Bloomberg)
To gain an edge in the markets, quantitative hedge fund managers require automated processing to quickly extract actionable information from unstructured and increasingly nontraditional sources of data. Arun Verma details AI and machine learning (ML) techniques in quantitative finance that lead to profitable trading strategies.
14:50-15:30 (40m) 实施人工智能 (Implementing AI), 英文讲话 (Presented in English)
Detect the undetectable at the breach
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.
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 (GO-JEK), Jewel James (Gojek)
Hear 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), 英文讲话 (Presented in English)
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 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.
13:10-13:50 (40m) 实施人工智能 (Implementing AI)
一个改善债务催收的AI解决方案(A humane AI solution to improve debt collection)
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.
14:00-14:40 (40m) 执行简报/最佳实践 (Executive Briefing/Best Practices)
线上财富管理领域中的AI应用
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。
14:50-15:30 (40m) 实施人工智能 (Implementing AI), 模型与方法 (Models and Methods)
Office Depot利用基于Apache Spark的深度学习实现实时产品推荐(Real-time product recommendations leveraging deep learning on Apache Spark in Office Depot)
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.
16:20-17:00 (40m) 实施人工智能 (Implementing AI)
AI如何彻底改变风电行业(How AI is revolutionizing the wind power industry)
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.
11:15-11:55 (40m) 企业人工智能 (AI in the Enterprise)
领英基于Spark和TensorFlow的大规模AI基础架构
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。
13:10-13:50 (40m) 与人工智能交互 (Interacting with AI), 模型与方法 (Models and Methods)
AI“美颜”你的歌声和视频:K歌修音和自动作曲
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。
14:00-14:40 (40m) 模型与方法 (Models and Methods)
深度学习语音技术在金融场景中的应用
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。
14:50-15:30 (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。
16:20-17:00 (40m) 实施人工智能 (Implementing AI)
Squirrel AI Learning的AI导师:AI-adaptive技术在K-12教育中的实际应用(Squirrel AI Learning’s AI tutors: Real-life applications of AI-adaptive technology in K–12 education)
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.
11:15-11:55 (40m) 英文讲话 (Presented in English)
A fresh approach to building high-performance AI systems (sponsored by Habana Labs)
Eitan Medina (Habana Labs)
The new class of purpose-built AI processors presents data center engineers and developers with opportunities to deliver tangible advancements in AI productivity and efficiency, resulting in lower total cost of ownership. Eitan Medina reveals the advantages derived from new approaches to building high-performance AI systems.
13:10-13:50 (40m)
基于数据中心基础架构的深度学习(由Dell Technologies赞助)(A deep learning harness built on data center infrastructure (sponsored by Dell Technologies))
Youhui Zhou (Dell)
Improve the utilization rate of data center resources. Join in to explore DL infrastructure and a GPU-as-a-service solution. You'll learn how it simplifies the AI compute requirements with automated access, better control, and simplified provisioning all while pushing your GPU resources to the limit accelerating your model training and inference.
14:00-14:40 (40m) 赞助商赞助 (Sponsored)
创邻Galaxybase图数据库和AI应用(由创邻科技赞助)(The world's fastest graph database Galaxybase and AI applications (sponsored by Chuang Lin Tech))
Chen Zhang (Chuang Lin Tech)
浙江创邻科技有限公司创始人张晨将介绍创邻科技自主知识产权的核心技术分布式图数据库Galaxybase。Galaxybase是目前世界上最快、延展性最好的图数据库,比Neo4j快20-100倍,高并发实时读写快1000倍,填补了我国图数据存储及处理领域的空白,并打造了国内首个专注图挖掘的认知计算平台。核心团队由海归大数据专家、国家青年千人、浙江省千人专家、杭州市特聘专家,及国内外名校博士、硕士组成,在海量数据并发并行处理、人工智能、图运算等领域有多项世界领先的技术储备。2018年获得了百度风投BV投资。演讲将介绍图数据库的技术背景、经典应用、Galaxybase的技术、应用和未来的挑战。
14:50-15:30 (40m)
Session
16:20-17:00 (40m) 实施人工智能 (Implementing AI)
基于Spark使用AI来玩游戏(Game playing using AI on Spark)
Shan Yu (Intel)
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.
08:00-08:45 (45m)
Break: Morning Coffee
08:00-08:30 (30m)
快速社交 (Speed Networking)
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。
08:45-08:50 (5m)
Thursday opening remarks
Ben Lorica (O'Reilly Media), Jason (Jinquan) Dai (Intel), Roger Chen (Computable)
Program chairs Ben Lorica, Jason Dai, and Roger Chen open the first day of keynotes.
08:50-09:05 (15m)
统一大数据分析和人工智能从而更快地大规模洞察(Unifying analytics and AI on big data for faster insights at scale)
马子雅 (Ziya Ma) (Intel)
Ziya Ma walks you through Intel’s scalable data insights strategy and related big data analytics and AI technologies such as Analytics Zoo—an end-to-end analytics and AI pipeline for developing full solutions with Apache Spark on Intel Xeon and Intel Optane DC Persistent Memory at scale. She highlights customers use cases and collaboration with industry leaders throughout.
09:05-09:15 (10m) 英文讲话 (Presented in English)
Accelerating AI adoption
Ben Lorica (O'Reilly Media), Roger Chen (Computable)
Accelerating AI Adoption
09:15-09:20 (5m)
解锁数据的力量; 拥抱智能+(由Dell Technologies赞助)(Unlock the power of data; embrace intelligent+ (sponsored by Dell Technologies))
Frank Wu (Dell Technologies)
This is the data era. Data helps to make better products and services, allowing a company to attract more customers, which results in more data—and repeat. Eventually, this turns into data capital, the most valuable corporate asset. Frank Wu explains why how you use your data will determine your future.
09:20-09:35 (15m)
云服务加速人工智能创新(Accelerate innovations with AI in the cloud)
Long Wang (Tencent)
We all know that the cloud is the best place to use new technologies. Long Wang examines what's happening for AI in the cloud: How does AI in the cloud accelerate the innovations in the industry? What's mostly possible? What's still on the way? How does the cloud help?
09:35-09:45 (10m) 赞助商赞助 (Sponsored)
Increasing AI productivity and efficiency with purpose-built AI processors (sponsored by Habana Labs)
Eitan Medina (Habana Labs)
Eitan Medina details advances made possible with AI processors designed to address AI-specific computing requirements, chief among them increasing AI throughput speeds while lowering power consumption. This new class of AI processing brings significantly improved productivity and efficiency to the data center to overcome limitations of existing CPU- and GPU-based solutions.
09:45-10:05 (20m) 英文讲话 (Presented in English)
The future of hiring and the talent market with AI
Maria Zhang (LinkedIn)
If the most dramatic headlines were true, we’d all be preparing for robots to take over our jobs, our lives, and, eventually, the world. But the truth is, automation and AI are doing more to improve the quality of our work than they are to replace us. Maria Zhang examines AI and its impact on people’s jobs, quality of work, and overall business outcomes.
10:05-10:20 (15m) 英文讲话 (Presented in English)
The future of machine learning is tiny
Pete Warden (Google)
There are over 250 billion embedded devices in the world. On-device machine learning gives us the ability to turn wasted data into actionable information and will enable a massive number of new applications over the next few years. Pete Warden digs into why embedded machine learning is so important, how to implement it on existing chips, and some of the new use cases it will unlock.
10:20-10:40 (20m) 英文讲话 (Presented in English)
AI and systems at RISELab
Ion Stoica (University of California, Berkeley)
Ion Stoica outlines a few projects at the intersection of AI and systems that RISELab, at the University of California, Berkeley, is developing. RISELab is the successor of AMPLab, where several highly successful open source projects, including Apache Spark and Apache Mesos, were developed.
10:40-10:45 (5m)
Closing remarks
Ben Lorica (O'Reilly Media), Jason (Jinquan) Dai (Intel), Roger Chen (Computable)
Program chairs Ben Lorica, Jason (Jinquan) Dai, and Roger Chen close the first day of keynotes.
10:45-11:15 (30m)
Break: Morning Break - Sponsored by Dell Technologies
15:30-16:20 (50m)
Break: Afternoon Break
11:55-13:10 (1h 15m)
周四午餐主题桌会 (Thursday 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。
17:00-17:45 (45m)
与会来宾欢乐时光(Attendee Happy Hour)
周四最后一节分会场议题后请加入我们的赞助商展馆招待会。与其他与会来宾展开社交,同时参观人工智能领域的创新公司。本次活动向所有赞助商、参展商及与会来宾开放。 Come enjoy snacks and beverages with fellow AI Conference attendees, speakers, and sponsors.