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 A practical guide toward explainability and bias evaluation in machine learning to your personal schedule
09:00 A practical guide toward explainability and bias evaluation in machine learning Alejandro Saucedo (The Institute for Ethical AI & Machine Learning)
Add Design thinking for AI to your personal schedule
13:30 Design thinking for AI Chris Butler (IPsoft)
报告厅(Auditorium)
Add 基于深度学习的时间序列预测 (Deep learning for time series forecasting) to your personal schedule
09:00 基于深度学习的时间序列预测 (Deep learning for time series forecasting) Yijing Chen (Microsoft), Dmitry Pechyoni (Microsoft), Angus Taylor (Microsoft), Vanja Paunic (Microsoft), Henry Zeng (Microsoft)
多功能厅8A+B(Function Room 8A+B)
多功能厅5C(Function Room 5C)
10:30 Morning Break | Room: TBD
12:30 Lunch | Room: TBD
15:00 Afternoon Break | Room: TBD
09:00-12:30 (3h 30m) 与人工智能交互 (Interacting with AI), 实施人工智能 (Implementing AI), 模型与方法 (Models and Methods), 英文讲话 (Presented in English)
A practical guide toward explainability and bias evaluation in machine learning
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.
13:30-17:00 (3h 30m) 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)
Design thinking for AI
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.
09:00-12:30 (3h 30m) 中英文讲话(Presented in Chinese and English)
基于深度学习的时间序列预测 (Deep learning for time series forecasting)
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.
13:30-17:00 (3h 30m) 实施人工智能 (Implementing AI), 模型与方法 (Models and Methods)
通过自动化机器学习民主化和加速AI落地 (Democratizing and accelerating AI through automated machine learning)
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.
09:00-12:30 (3h 30m) 实施人工智能 (Implementing AI)
Analytics Zoo: Distributed TensorFlow and Keras on Apache Spark
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.)
13:30-17:00 (3h 30m) 英文讲话 (Presented in English)
Building reinforcement learning models and AI applications with Ray
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.
09:00-12:30 (3h 30m)
Intel OpenVINO: Accelerating deep learning inference and computer vision from edge to cloud
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.
10:30-11:00 (30m)
Break: Morning Break
12:30-13:30 (1h)
Break: Lunch
15:00-15:30 (30m)
Break: Afternoon Break