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 towards explainability and bias evaluation in machine learning to your personal schedule
09:00 A practical guide towards 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)
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 towards 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 due to the numerous high profile incidents. In this talk we demystify machine learning bias through a hands-on example. We'll be tasked to automate the loan approval process for a company, and introduce key tools and techniques from latest research that allow us to assess and mitigate undesired bias in our 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 from people are important factors to any system, especially those that employ AI. Chris Butler leads you through exercises that borrow from the principles of design thinking to help you create more impactful solutions and better team alignment.
09:00-12:30 (3h 30m) 模型与方法 (Models and Methods)
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 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. In this tutorial we show how and when to apply deep neural networks to time series forecasting. The tutorial will be in CHN and EN.
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)
Sujatha Sagiraju (Microsoft), Henry Zeng (Microsoft)
Intelligent experiences powered by AI can seem like magic to users. Developing them, however, is pretty cumbersome involving a series of sequential and interconnected decisions along the way that are pretty time consuming. What if there was an automated service that identifies the best machine learning pipelines for a given problem/data? Automated machine learning does exactly that!
09:00-12:30 (3h 30m) 实施人工智能 (Implementing AI), 英文讲话 (Presented in English)
Analytics Zoo: Distributed Tensorflow and Keras on Apache Spark
Zhichao Li (Intel)
In this tutorial, we will show how to build and productionize deep learning applications for Big Data using "Analytics Zoo":https://github.com/intel-analytics/analytics-zoo (a unified analytics + AI platform that seamlessly unites Spark, TensorFlow, Keras and BigDL programs into an integrated pipeline) using 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. We will lead 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.
10:30-11:00 (30m)
Break: Morning Break
12:30-13:30 (1h)
Break: Lunch
15:00-15:30 (30m)
Break: Afternoon Break