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 Efficient deep learning for the edge to your personal schedule
13:10 Efficient deep learning for the edge Bichen Wu (UC Berkeley)
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 Li 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 Exciting new features in TensorFlow 2.0 to your personal schedule
14:00 Exciting new features in TensorFlow 2.0 Tiezhen Wang (Google)
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 Game playing using AI on Spark to your personal schedule
16:20 Game playing using AI on Spark Shengsheng Huang (Intel)
多功能厅5A+B(Function Room 5A+B)
Add 视频精彩度分析及智能创作 to your personal schedule
11:15 视频精彩度分析及智能创作 刘祁跃 (爱奇艺)
Add A humane AI solution to improve debt collection to your personal schedule
13:10 A humane AI solution to improve debt collection Ying Liu (Abakus 鲸算科技(Wecash闪银))
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)
Add Real-time product recommendations leveraging deep learning on Apache Spark in Office Depot to your personal schedule
14:50 Real-time product recommendations leveraging deep learning on Apache Spark in Office Depot Guoqiong Song (Intel), Luyang Wang (Office Depot), Jiao(Jennie) Wang (Intel), Jing (Nicole) Kong (Office Depot)
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)
14:50
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
08:45 Thursday opening remarks | Room: 紫金大厅A(Grand Hall A) Ben Lorica (O'Reilly Media), Jason (Jinquan) Dai (Intel), Roger Chen (Computable)
Add The Future of AI to your personal schedule
08:50 The Future of AI | Room: 紫金大厅A(Grand Hall A) Abby Wen (Intel Corp.), Julie Shin Choi (Intel AI)
Add Keynote by program chairs Ben Lorica and Roger Chen to your personal schedule
09:05 Keynote by program chairs Ben Lorica and Roger Chen | Room: 紫金大厅A(Grand Hall A)
Add Keynotes to come to your personal schedule
09:15 Keynotes to come | Room: 紫金大厅A(Grand Hall A)
Add Keynote by Long Wang, VP Tencent Cloud to your personal schedule
09:20 Keynote by Long Wang, VP Tencent Cloud | Room: 紫金大厅A(Grand Hall A) Long Wang (Tencent)
Add Keynote to come to your personal schedule
09:35 Keynote to come | Room: 紫金大厅A(Grand Hall A)
Add Keynote by Maria Zhang to your personal schedule
09:45 Keynote by Maria Zhang | Room: 紫金大厅A(Grand Hall A) Maria Zhang (LinkedIn)
Add The Future of Machine Learning is Tiny to your personal schedule
10:05 The Future of Machine Learning is Tiny | Room: 紫金大厅A(Grand Hall A) Pete Warden (Google)
Add Keynote with Ion Stoica to your personal schedule
10:20 Keynote with Ion Stoica | Room: 紫金大厅A(Grand Hall A) Ion Stoica (UC Berkeley)
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 周四午餐主题桌会 (Thursday Topic Tables at Lunch) to your personal schedule
11:55 Lunch 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 Sponsor Pavilion Reception to your personal schedule
17:00 Sponsor Pavilion Reception | 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 improves 25% of forecasting accuracy with dilated convolutional neural networks and reduces 80% of time in development with best practices of time series forecasting.
13:10-13:50 (40m) 模型与方法 (Models and Methods), 英文讲话 (Presented in English)
Efficient deep learning for the edge
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.
14:00-14:40 (40m)
TensorFlow lite for microcontrollers
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.
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 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.
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 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.
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
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. The session will be delivered in English and Chinese jointly.
14:00-14:40 (40m) 实施人工智能 (Implementing AI)
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. He explores distributed strategies and edge deployment (TensorFlow Lite and TensorFlow.js).
14:50-15:30 (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。
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 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.
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 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.
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)
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.
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, 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?
16:20-17:00 (40m) 实施人工智能 (Implementing AI), 英文讲话 (Presented in English)
Game playing using AI on Spark
Shengsheng Huang (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. Shengsheng Huang takes you through her experiences from her attempts in using the AI on Spark for playing games.
11:15-11:55 (40m) 实施人工智能 (Implementing AI)
视频精彩度分析及智能创作
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
13:10-13:50 (40m) 实施人工智能 (Implementing AI)
A humane AI solution to improve debt collection
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.
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)
Real-time product recommendations leveraging deep learning on Apache Spark in Office Depot
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.
16:20-17:00 (40m) 实施人工智能 (Implementing AI)
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, so you don't have to.
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)
Session
16:20-17:00 (40m) 实施人工智能 (Implementing AI)
Squirrel AI Learning’s AI tutors: Real-life applications of AI-adaptive technology in K–12 education
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.
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)
Opening keynote remarks by program chairs Ben Lorica, Jason Dai, and Roger Chen.
08:50-09:05 (15m) 英文讲话 (Presented in English)
The Future of AI
Abby Wen (Intel Corp.), Julie Shin Choi (Intel AI)
The Future of AI, a discussion with Abigail Wen and Julie Choi
09:05-09:15 (10m)
Keynote by program chairs Ben Lorica and Roger Chen
Keynote by program chairs Ben Lorica and Roger Chen
09:15-09:20 (5m)
Keynotes to come
Keynotes to come
09:20-09:35 (15m)
Keynote by Long Wang, VP Tencent Cloud
Long Wang (Tencent)
Keynote by Long Wang, VP Tencent Cloud
09:35-09:45 (10m)
Keynote to come
Keynotes to come
09:45-10:05 (20m)
Keynote by Maria Zhang
Maria Zhang (LinkedIn)
Keynote by Maria Zhang
10:05-10:20 (15m) 英文讲话 (Presented in English)
The Future of Machine Learning is Tiny
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.
10:20-10:40 (20m)
Keynote with Ion Stoica
Ion Stoica (UC Berkeley)
Keynote with Ion Stoica
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)
Sponsor Pavilion Reception
Come enjoy snacks and beverages with fellow AI Conference attendees, speakers, and sponsors.