Break Barriers between Model and Reality June 18-21, 2019 in Beijing

Break Barriers between Model and Reality

June 18-21, 2019 in Beijing

AI is moving fast. At the Artificial Intelligence Conference, co-presented by O’Reilly Media and Intel Corporation, you’ll find unparalleled technical content with an applied industry focus, including the new breakthroughs, tools, frameworks, use cases, and business applications you need to put AI to work now.

Follow us @IntelAI for the latest happenings at #TheAIConf in Beijing!

Intel Booth and Demos

This year’s Artificial Intelligence Conference takes place at the Beijing International Hotel. We invite you to visit our booth #200 in the sponsor pavilion on Thursday, June 20 and Friday, June 21 for demonstrations from Intel and our partners. Please join us for a reception on Thursday, June 20th from 17:00 – 17:45.

Booth demonstrations include:

Company Title
Intel Inference Performance Boost with Intel® DL Boost and 2nd Generation Intel® Xeon® Scalable Processors
Intel AI Gaming with Analytics Zoo (Apache Spark* + Big DL unified platform)
TenCent YouTu AI Camera based on Intel® Movidius™ Myriad™ VPU
LLVision AR+AI Glasses GLXSS ME + Edge server
Tongfang SighTA: Smart Video Analysis based on Intel® Xeon® Processors
ZTE Edge Solution for Video Analysis based on Intel Processors and Intel® Stratix® 10 FPGAs
4 Paradigm SageOne: AI All-in-One System based on Intel Xeon Processors and Intel® 3D Xpoint™ Memory

Wednesday, June 19


Analytics Zoo: Distributed TensorFlow* and Keras* on Apache Spark*

Time: 09:00 – 12:30
Location: Function Room 8A+B
Speaker: Zhichao Li – Intel
Abstract: Analytics Zoo provides a unified analytics and AI platform that seamlessly unites Spark, TensorFlow, Keras, and BigDL programs into an integrated pipeline; the entire pipeline can then transparently scale out to a large Hadoop/Spark cluster for distributed training or inference. Attendees will learn how to build and productionize deep learning applications for big data using Analytics Zoo and look at real-world use cases.


Intel® Distribution of OpenVINO™: Accelerating Deep Learning Inference and Computer Vision from Edge to Cloud

Time: 09:00 – 12:30
Location: Function Room 5C
Speaker: Zhen Zhao – Intel
Abstract: Intel’s OpenVINO toolkit helps developers efficiently deploy deep neural network inference models and accelerate the deep learning and computer vision application from edge to cloud applications based on various Intel hardware platforms, including CPUs, GPUs, vision processing units (VPUs), and field-programmable gate arrays (FPGAs). This session shares the structure and workflow of the toolkit, optimization methods, and more.

Thursday, June 20


The Future of AI

Time: 08:50 – 09:05
Location: Grand Hall A
Speakers: Julie Shin Choi – Intel
Abigail Huang Wen – Intel

Julie Shin Choi is VP and GM of Artificial Intelligence Products and Research Marketing at Intel Corporation. She is responsible for marketing the Intel portfolio of hardware and software products for building enterprise scale AI solutions and helping customers, developers, and the ecosystem understand Intel’s rich set of AI offerings.

Abigail Huang Wen is Managing Counsel, Office of the CTO, in the Artificial Intelligence Products Group at Intel Corporation. She serves as a strategic, legal and policy adviser for AI research efforts, focusing on emerging technologies, the greater ecosystem and policy leadership.


Real-time Product Recommendations Leveraging Deep Learning on Apache Spark in Office Depot

Time: 14:50 – 15:30
Location: Function Room 5A+B
Speakers: Guoqiong Song – Intel
Luyang Wang – Office Depot
Jiao (Jennie) Wang – Intel
Jing (Nicole) Kong – Office Depot
Abstract: Real-time recommender systems are critical for the success of the e-commerce industry. Newly developed deep neural networks (DNNs) have shown success as recommender systems by capturing nonlinear relationships in the user-item dataset. In this session, attendees will learn how to build efficient recommender systems for the e-commerce industry using deep learning technologies.


Game-Playing using AI on Spark

Time: 16:20 – 17:00
Location: Function Room 2
Speaker: Shengsheng Huang – Intel
Abstract: 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 shares experiences from her attempts in using AI on Spark for game playing.

Friday, June 21


Unifying Analytics & AI on Big Data for Faster Insights at Scale

Time: 08:50 – 09:00
Location: Grand Hall A
Speaker: Ziya Ma – Intel
Bios: Ziya Ma is vice president in Intel Architecture, Graphics and Software as well as director of Data Analytics Technologies in System Software Products at Intel Corp. Ma is responsible for optimizing big data solutions on the Intel® architecture platform, leading open source efforts in the Apache community, and bringing about optimal big data analytics and AI experiences for customers.


Analytics Zoo: Distributed TensorFlow in production on Apache Spark

Time: 11:15 – 11:55
Location: Auditorium
Speaker: Yang Wang – Intel
Abstract: Building a model is fun and exciting; putting it to production is always a different story. This session introduces Analytics Zoo, a unified analytics and AI platform for distributed TensorFlow, Keras, and BigDL on Apache Spark. This new framework enables easy experimentation for algorithm designs and supports training and inference on Spark clusters with ease of use and near-linear scalability.


Enabling Deep Learning at the Edge

Time: 13:10 – 13:50
Location: Function Room 6A + B
Speaker: Yurong Chen – Intel
Abstract: Attendees will learn three key ways to enable deep learning inference at the edge devices from DNN algorithm design perspective.


Low-precision Inference on Intel Architecture

Time: 16:20 – 17:00
Location: Function Room 6A + B
Speaker: Lei Xia – Intel
Abstract: This session provides a brief introduction of low-precision for AI inference, introduces Intel® Deep Learning Boost featuring Vector Neural Network Instructions (VNNI), and introduces how to use low-precision inference acceleration with Intel Optimization for Caffe, TensorFlow*, and Intel® Distribution of OpenVINO™.