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)
11:15
Add Efficient Deep Learning for the Edge to your personal schedule
13:10 Efficient Deep Learning for the Edge Bichen Wu (UC Berkeley)
14:00
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 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 Industrialized Capsule Networks for Text Analytics to your personal schedule
14:00 Industrialized Capsule Networks for Text Analytics Vijay Agneeswaran (Publicis Sapient), Abhishek Kumar (Publicis Sapient)
14:50
16:20
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), Jennie Wang (Intel), Jing (Nicole) Kong (Office Depot)
Add How AI is Revolutionizing the Wind Power Industry   to your personal schedule
16:20 How AI is Revolutionizing the Wind Power Industry YAN KE (上海扩博智能技术有限公司)
多功能厅6A+B (Function Room 6A+B)
11:15
14:50
Add Game playing using AI on Spark to your personal schedule
16:20 Game playing using AI on Spark Shengsheng Huang (Intel)
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 Keynotes to come to your personal schedule
08:50 Keynotes to come | Room: 紫金大厅A(Grand Hall A)
10:45 Morning Break | Room: 报告厅序厅 (Auditorium Foyer)
15:30 Afternoon Break | Room: 报告厅序厅 (Auditorium Foyer)
11:55 Lunch | Room: 彩虹厅及国际厅 (Rainbow Room & Ballroom)
11:15-11:55 (40m)
Session
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. In this talk we discuss how we apply AutoML, SW/HW codesign, domain adaptation to solve these problems.
14:00-14:40 (40m)
Session
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. Using deep learning and time-series forecasting, we'll show 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.ai)
Zero-day attacks. IoT-based botnets. Cybercriminal AI v. cyberdefender AI. While these won’t be going away, they aren’t the biggest worry we have in cybercrime. Hacking humans is. The combination of mere minutes of video, signal processing, remote heart rate monitoring, AI, machine learning, 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)
Prasanth Pulavarthi (Microsoft), Henry Zeng (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. This session provides an introduction 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. This talk will give a in depth introduction to the new exciting features and best practices. Topics such as distributed strategies and edge deployment (TensorFlow Lite and TensorFlow.js) will also be covered.
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 NLP
David Low (Pand.ai)
Transfer Learning has been proven to be a tremendous success in the Computer Vision field as a result of ImageNet competition. In the past months, the Natural Language Processing field has witnessed several breakthroughs with transfer learning, namely ELMo, Transformer, ULMFit and BERT. In this talk, David will be showcasing the use of transfer learning on NLP application with SOTA 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. Alex Ingerman introduces 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)
Industrialized Capsule Networks for Text Analytics
Vijay Agneeswaran (Publicis Sapient), Abhishek Kumar (Publicis Sapient)
We illustrate how capsule networks can be industrialized: 1. Overview of capsule networks and how they help in handling spatial relationships between objects in an image. We also learn about how they can be applied to text analytics. 2. We show an implementation of recurrent capsule networks, which are useful in text analytics, especially for some tasks such as summarization or classification.
14:50-15:30 (40m) 模型与方法 (Models and Methods), 英文讲话 (Presented in English)
Session
16:20-17:00 (40m) 实施人工智能 (Implementing AI), 英文讲话 (Presented in English)
Session
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))
AI debt collection platform of Abakus provides a friendly and humane product solution which is designed for people who work in the live agents of the organization in the frontline. The agent training of the organization could be enhanced more smoothly with an AI friendly culture. It has been proved in our experiment that 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), Jennie Wang (Intel), Jing (Nicole) Kong (Office Depot)
To show case how to build efficient recommender systems for e-commerce 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
YAN KE (上海扩博智能技术有限公司)
In this talk, we will share 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)
Session
13:10-13:50 (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 one of the most important and common business problems. In Microsoft Azure Identity team, we forecast customer behavior based on billions of user activities. We will share how we improve 25% of forecasting accuracy with dilated convolutional neural networks and reduce 80% of the time in development with the best practices of time series forecasting.
14:00-14:40 (40m) 模型与方法 (Models and Methods)
Confidence Estimation for Deep Neural Networks
Jialin Jiao (Pony.ai)
While deep learning has been in the center of AI with unprecedentedly great results, predictions of deep neural networks usually do not come with a reliable and well-calibrated confidence score. Wrong but confident predictions place great threads to critical real-life applications, e.g. self-driving car. This talk is a tutorial/comparison of confidence estimation methods for deep neural networks.
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)
Game playing using AI on Spark
Shengsheng Huang (Intel)
In this presentation we will share experiences from our attempts in using AI on Spark for game playing.
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
08:50-10:45 (1h 55m)
Keynotes to come
Keynotes to come
10:45-11:15 (30m)
Break: Morning Break
15:30-16:20 (50m)
Break: Afternoon Break
11:55-13:10 (1h 15m)
Break: Lunch