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April 10-11, 2018: Training
April 11-13, 2018: Tutorials & Conference
Beijing, CN

Schedule: 英文讲话 (Presented in English) sessions

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09:0012:30 Wednesday, April 11, 2018
Location: 报告厅(Auditorium)
Secondary topics:  深度学习(Deep Learning)
Yufeng Guo (Google)
Yufeng Guo walks you through training a machine learning system using popular open source library TensorFlow, starting from conceptual overviews and building all the way up to complex classifiers. Along the way, you'll gain insight into deep learning and how it can apply to complex problems in science and industry. Read more.
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09:0017:00 Wednesday, April 11, 2018
Location: 多功能厅8A+B(Function Room 8A+B) Level: Intermediate
Secondary topics:  传媒、广告、娱乐 (Media, Advertising, Entertainment), 制造业与工业自动化 (Manufacturing and Industrial Automation), 设计AI平台(Designing AI platforms), 金融服务 (Financial Services)
Kristian Hammond (Northwestern Computer Science)
Average rating: *****
(5.00, 2 ratings)
Even as AI technologies move into common use, many enterprise decision makers remain baffled about what the different technologies actually do and how they can be integrated into their businesses. Rather than focusing on the technologies alone, Kristian Hammond provides a practical framework for understanding your role in problem solving and decision making. Read more.
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13:3017:00 Wednesday, April 11, 2018
Location: 报告厅(Auditorium) Level: Intermediate
Secondary topics:  增强学习(Reinforcement Learning)
Arthur Juliani (Unity Technologies), Leon Chen (Unity Technologies)
Recently, computers have been able to learn to play Atari games, Go, and first-person shooters at a superhuman level. Underlying all these accomplishments is deep reinforcement learning. Arthur Juliani and Leon Chen lead a deep dive into reinforcement learning, from the basics using lookup tables and GridWorld all the way to solving complex 3D tasks with deep neural networks. Read more.
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08:5509:10 Thursday, April 12, 2018
Location: 紫金大厅A(Grand Hall A)
Arjun Bansal (Intel)
Artificial intelligence is transforming every industry, but the role it will play in healthcare is profound. Arjun Bansal explains how AI can give physicians new insights and speed time to diagnosis by leveraging vast amounts of healthcare data and how it can reduce the time and money spent to develop new medicines. Read more.
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09:1009:20 Thursday, April 12, 2018
Location: 紫金大厅A(Grand Hall A)
Ben Lorica (O'Reilly Media), Roger Chen (Computable Labs)
Details to come. Read more.
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09:2009:30 Thursday, April 12, 2018
Location: 紫金大厅A(Grand Hall A)
Erran Li (Uber ATG)
We have made rapid progress in apply machine learning to solve perception, prediction and planning problems. However, there are fundamental challenges ahead. We need to learn more robust and abstract representations, understand driving scenes, and make decisions in multi-agent settings. Read more.
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09:3009:45 Thursday, April 12, 2018
Location: 紫金大厅A(Grand Hall A)
Secondary topics:  计算机视觉(Computer Vision)
Reza Zadeh (Matroid | Stanford)
Reza Zadeh details three challenges on the way to building cutting-edge ML products, with a focus on computer vision, offering examples, recommendations, and lessons learned. Read more.
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10:1510:30 Thursday, April 12, 2018
Location: 紫金大厅A(Grand Hall A)
Secondary topics:  增强学习(Reinforcement Learning)
Danny Lange (Unity Technologies)
Danny Lange offers an overview of deep reinforcement learning, an exciting new chapter in AI’s history that is changing the way we develop and test learning algorithms that can later be used in real life. Read more.
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11:1511:55 Thursday, April 12, 2018
Location: 紫金大厅B(Grand Hall B) Level: Beginner
Secondary topics:  设计AI平台(Designing AI platforms)
Simon Chan (Salesforce)
Building an end-to-end AI application in production is tremendously more complicated than simply doing algorithm modeling in a lab. Simon Chan explains how to cross the gap between AI research fantasy into real-world applications. Read more.
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11:1511:55 Thursday, April 12, 2018
Location: 多功能厅2(Function Room 2)
Secondary topics:  AI应用的硬件、软件栈(Hardware and Software stack for AI applications), 计算机视觉(Computer Vision)
Reza Zadeh (Matroid | Stanford)
Reza Zadeh offers an overview of Matroid’s Kubernetes deployment, which provides customized computer vision and stream monitoring to a large number of users, and demonstrates how to customize computer vision neural network models in the browser. Along the way, Reza explains how Matroid builds, trains, and visualizes TensorFlow models, which are provided at scale to monitor video streams. Read more.
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13:1013:50 Thursday, April 12, 2018
Location: 紫金大厅B(Grand Hall B) Level: Intermediate
Secondary topics:  传媒、广告、娱乐 (Media, Advertising, Entertainment), 自然语言与语音技术(Natural Language and Speech Technologies), 金融服务 (Financial Services)
Zhefu Shi (University of Missouri)
It is critical to analyze the business impact of worldwide events on the financial market. Zhefu Shi explains how to use AI to analyze the impact of financial news using a financial data pipeline. Zhefu outlines how to extract financial entity information and use it to analyze business impact. All of the components use AI to enhance functionality. Read more.
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13:1013:50 Thursday, April 12, 2018
Location: 报告厅(Auditorium) Level: Intermediate
Secondary topics:  AI应用的硬件、软件栈(Hardware and Software stack for AI applications)
Tags: wl
Ruiwen Zhang (SAS Institute)
Drawing on several real-world cases, Ruiwen Zhang demonstrates how to visualize the structure of a probabilistic model and provide better insights into the model's properties, which can be further used to design and motivate new models. She also explains how to reduce the computational complexity required to perform inference and learning in sophisticated models using graphical models. Read more.
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13:1013:50 Thursday, April 12, 2018
Location: 多功能厅2(Function Room 2) Level: Intermediate
Secondary topics:  自然语言与语音技术(Natural Language and Speech Technologies)
Emmanuel Ameisen (Insight Data Science), Yan Kou (Insight Data Science)
Emmanuel Ameisen and Yan Kou share a guide for moving your company toward deep learning using a collection of NLP best practices gathered from conversations with 75+ teams from Google, Facebook, Amazon, Twitter, Salesforce, Airbnb, Capital One, Bloomberg, and others. Read more.
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14:0014:40 Thursday, April 12, 2018
Location: 紫金大厅B(Grand Hall B) Level: Intermediate
Secondary topics:  自然语言与语音技术(Natural Language and Speech Technologies)
David Talby (Pacific AI)
Average rating: *****
(5.00, 1 rating)
Natural language processing is a key component in many data science systems that must understand or reason about text. David Talby offers an overview of the NLP library for Apache Spark, which natively extends Spark ML to provide open source, fully distributed, and optimized versions of state-of-the-art NLP algorithms, covering the library's design and sharing working code samples in PySpark. Read more.
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14:0014:40 Thursday, April 12, 2018
Location: 多功能厅2(Function Room 2)
Secondary topics:  传媒、广告、娱乐 (Media, Advertising, Entertainment), 增强学习(Reinforcement Learning)
Danny Lange (Unity Technologies)
Danny Lange demonstrates the role games can play in driving the development of reinforcement learning algorithms. Danny uses the Unity Engine with the ML-Agents toolkit as an example of how dynamic 3D game environments can be utilized for machine learning research. Read more.
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14:5015:30 Thursday, April 12, 2018
Location: 多功能厅2(Function Room 2) Level: Beginner
Secondary topics:  自然语言与语音技术(Natural Language and Speech Technologies)
Yishay Carmiel (IntelligentWire)
Yishay Carmiel offers an overview of neural models in speech applications, covering the dominant techniques and the elements that have contributed to the rapid progress. Yishay also looks to the future, examining which problems still remain and how far we are from solving them. Read more.
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16:2017:00 Thursday, April 12, 2018
Location: 紫金大厅B(Grand Hall B) Level: Intermediate
Secondary topics:  电信 (Telecom), 自然语言与语音技术(Natural Language and Speech Technologies)
Sangkeun Jung (SK Telecom)
Natural language understanding is a core technology for building natural interfaces such as AI speakers, chatbots, and smartphones. Sangkeun Jung offers an overview of a spoken dialog system and recently launched AI speaker, NUGU, and shares lessons learned building a commercially efficient and sustainable natural language understanding system. Read more.
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16:2017:00 Thursday, April 12, 2018
Location: 多功能厅2(Function Room 2)
Secondary topics:  保健与医疗 (Health and Medicine)
Arjun Bansal (Intel)
Precision medicine promises to revolutionize healthcare by delivering better health outcomes at lower cost by eliminating trial-and-error medicine, and Intel is working to make this a reality. Arjun Bansal shares emerging algorithms and models used to analyze healthcare data, including electronic health records, medical images, and pharmaceutical and genomics datasets. Read more.
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09:0509:20 Friday, April 13, 2018
Location: 紫金大厅A(Grand Hall A)
Secondary topics:  制造业与工业自动化 (Manufacturing and Industrial Automation), 增强学习(Reinforcement Learning)
Mark Hammond (Microsoft)
Mark Hammond explores a wide breadth of real-world applications of deep reinforcement learning, including robotics, manufacturing, energy, and supply chain. Mark also shares best practices and tips for building and deploying these systems, highlighting the unique requirements and challenges of industrial AI applications. Read more.
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10:0010:15 Friday, April 13, 2018
Location: 紫金大厅A(Grand Hall A)
Hassan Sawaf (Amazon Web Services)
With today’s device and user interface technology and also the advent of advanced machine learning and deep learning models, input and output modalities are converging in many different dimensions. Hassan Sawaf offers a brief overview of research in human language technology and machine learning in merging information that is captured by the senses of machines. Read more.
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11:1511:55 Friday, April 13, 2018
Location: 紫金大厅B(Grand Hall B) Level: Beginner
Roger Chen (Computable Labs)
Blockchain technologies offer new internet primitives for creating open and online data marketplaces. Roger Chen explores how data markets can be constructed and how they offer a shared resource on the internet for AI-based research, discovery, and development. Read more.
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11:1511:55 Friday, April 13, 2018
Location: 多功能厅2(Function Room 2)
Hassan Sawaf (Amazon Web Services)
Hassan Sawaf discusses Amazon’s efforts to enable the enterprise with machine learning capability, in particular with newly released AWS services like Amazon Transcribe, Amazon Translate, Amazon Comprehend, Amazon Polly, Amazon Lex, Amazon SageMaker, and explains how some of these correlate with other Amazon products and services. Read more.
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13:1013:50 Friday, April 13, 2018
Location: 紫金大厅B(Grand Hall B) Level: Intermediate
Secondary topics:  AI应用的硬件、软件栈(Hardware and Software stack for AI applications)
Kaz Sato (Google)
The tensor processing unit (TPU) is a LSI designed by Google for neural network processing. The TPU features a large-scale systolic array matrix unit that achieves an outstanding performance-per-watt ratio. Kazunori Sato explains how a minimalistic design philosophy and a tight focus on neural network inference use cases enables the high-performance neural network accelerator chip. Read more.
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13:1013:50 Friday, April 13, 2018
Location: 多功能厅2(Function Room 2) Level: Intermediate
Secondary topics:  制造业与工业自动化 (Manufacturing and Industrial Automation), 增强学习(Reinforcement Learning)
Mark Hammond (Microsoft)
Mark Hammond dives into two case studies highlighting how deep reinforcement learning can be applied to real-world industrial applications. Read more.
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14:0014:40 Friday, April 13, 2018
Location: 多功能厅2(Function Room 2) Level: Intermediate
Secondary topics:  增强学习(Reinforcement Learning), 深度学习(Deep Learning)
Arsenii Mustafin (Fudan University)
Deep reinforcement learning is a thriving area and has wide applications in industry. Arsenii Mustafin shares his experience developing deep reinforcement learning applications on BigDL and Spark. Read more.
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14:5015:30 Friday, April 13, 2018
Location: 紫金大厅B(Grand Hall B)
Huma Abidi (Intel)
Intel has been optimizing deep learning frameworks (in collaboration with framework owners) for Intel Xeon processors based on its Skylake microarchitecture. Huma Abidi details these collaborative optimization efforts, particularly for TensorFlow and MXNet, explains how users can leverage these optimizations, and shares specific tuning tips to get the best performance on Skylake platforms. Read more.
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14:5015:30 Friday, April 13, 2018
Location: 多功能厅2(Function Room 2)
Le Zhang (Microsoft), Graham Williams (Microsoft)
Le Zhang and Graham Williams demonstrate how R-user data scientists and AI developers can use cloud services for convenient experimentation and production. Join Le and Graham for a walk-through of a proposed method that favors a TTD requirement for enterprise-grade AI and data applications. Read more.
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16:2017:00 Friday, April 13, 2018
Location: 紫金大厅B(Grand Hall B) Level: Beginner
Hendra Suryanto (Rich Data Corporation )
Hendra Suryanto shares a case study from a Canadian financial lender that his company helped transition from manual to automated credit decisioning, using gradient boosting machine and deep learning to build the model. In addition to modeling techniques, Hendra highlights the role feature engineering plays in improving model performance. Read more.
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16:2017:00 Friday, April 13, 2018
Location: 多功能厅2(Function Room 2) Level: Intermediate
Secondary topics:  保健与医疗 (Health and Medicine), 深度学习(Deep Learning)
Nishant Sahay (Wipro Limited)
Deep learning with ConvNet in particular has emerged as a promising tool in medical research labs and diagnostic centers to help analyze images and scans, and systems are now surpassing human capability for manual inspection. Nishant Sahay explains how to apply deep learning to analyze high-end microscope images and X-ray scans to provide accurate diagnosis. Read more.