English中文
将人工智能用起来
2019年6月18-21日
北京,中国

Schedule: 英文讲话 (Presented in English) sessions

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09:0012:30 Wednesday, June 19, 2019
Location: 紫金大厅B(Grand Hall B)
Alejandro Saucedo (The Institute for Ethical AI & Machine Learning)
Numerous high-profile incidents have proved undesired bias in machine learning a worrying topic. Alejandro Saucedo uses a hands-on example to demystify machine learning bias. You'll automate the loan-approval process for a company and explore key tools and techniques from the latest research that allows you to assess and mitigate undesired bias in machine learning models. 了解更多信息.
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13:3017:00 Wednesday, June 19, 2019
Location: 紫金大厅B(Grand Hall B)
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. 了解更多信息.
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13:3017:00 Wednesday, June 19, 2019
Location: 多功能厅8A+B(Function Room 8A+B)
Richard Liaw (UC Berkeley RISELab), Siyuan Zhuang (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. 了解更多信息.
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09:0509:15 Thursday, June 20, 2019
Location: 紫金大厅A(Grand Hall A)
Ben Lorica (O'Reilly), Roger Chen (Computable)
Accelerating AI Adoption 了解更多信息.
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09:4510:05 Thursday, June 20, 2019
Location: 紫金大厅A(Grand Hall A)
Maria Zhang (LinkedIn)
Average rating: *****
(5.00, 1 rating)
If the most dramatic headlines were true, we’d all be preparing for robots to take over our jobs, our lives, and, eventually, the world. But the truth is, automation and AI are doing more to improve the quality of our work than they are to replace us. Maria Zhang examines AI and its impact on people’s jobs, quality of work, and overall business outcomes. 了解更多信息.
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10:0510:20 Thursday, June 20, 2019
Location: 紫金大厅A(Grand Hall A)
Pete Warden (Google)
There are over 250 billion embedded devices in the world. On-device machine learning gives us the ability to turn wasted data into actionable information and will enable a massive number of new applications over the next few years. Pete Warden digs into why embedded machine learning is so important, how to implement it on existing chips, and some of the new use cases it will unlock. 了解更多信息.
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10:2010:40 Thursday, June 20, 2019
Location: 紫金大厅A(Grand Hall A)
Ion Stoica (University of California, Berkeley)
Ion Stoica outlines a few projects at the intersection of AI and systems that RISELab, at the University of California, Berkeley, is developing. RISELab is the successor of AMPLab, where several highly successful open source projects, including Apache Spark and Apache Mesos, were developed. 了解更多信息.
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11:1511:55 Thursday, June 20, 2019
Location: 紫金大厅B(Grand Hall B)
Tao Lu (Microsoft), Chenhui Hu (Microsoft)
Average rating: ***..
(3.75, 4 ratings)
Forecasting customer activities is an important, common business problem, and Tao Lu and Chenhui Hu forecast customer behavior based on billions of user activities. Join them as they share how Microsoft improved forecasting accuracy by 25% with dilated convolutional neural networks and reduced time in development by 80% with a set of time series forecasting best practices. 了解更多信息.
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11:1511:55 Thursday, June 20, 2019
Location: 多功能厅2(Function Room 2)
David Low (Pand.ai)
Average rating: *****
(5.00, 1 rating)
Transfer learning has been a tremendous success in computer vision as a result of the ImageNet competition. In the past few months, natural language processing (NLP) has witnessed several breakthroughs with transfer learning, namely ELMo, Transformer, ULMFit, and BERT. Join David Low as he showcases the use of transfer learning on NLP applications with state-of-the-art accuracy. 了解更多信息.
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11:1511:55 Thursday, June 20, 2019
Location: 多功能厅5A+B(Function Room 5A+B)
Renjei Li (NetEase Fuxi Lab)
Theoretical AI research isn't a bottleneck in AI, but finding a good application scenario for AI is. Renjei Li explains why gaming is a great scenario for AI and walks you through recent research in the future of AI games involving reinforcement learning, natural language processing (NLP), computer vision and graphics, and user personas and virtual humans. 了解更多信息.
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11:1511:55 Thursday, June 20, 2019
Location: 多功能厅8A+B(Function Room 8A+B)
Eitan Medina (Habana Labs)
The new class of purpose-built AI processors presents data center engineers and developers with opportunities to deliver tangible advancements in AI productivity and efficiency, resulting in lower total cost of ownership. Eitan Medina reveals the advantages derived from new approaches to building high-performance AI systems. 了解更多信息.
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13:1013:50 Thursday, June 20, 2019
Location: 紫金大厅B(Grand Hall B)
Bas Geerdink (Aizonic)
Average rating: *****
(5.00, 1 rating)
ING is a data-driven enterprise, with analytics skills as a top strategic priority. AI is at the core of ING’s business, and the company is investing in AI, big data, and analytics to improve business processes such as balance forecasting, fraud detection, and customer relation management. Follow along with (and be inspired by) Bas Geerdink's overview of the company's use cases and technology. 了解更多信息.
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13:1013:50 Thursday, June 20, 2019
Location: 多功能厅2(Function Room 2)
Alex Ingerman (Google)
Federated learning involves training ML models across a fleet of participating devices without collecting their data in a central location. Alex Ingerman examines 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. 了解更多信息.
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14:0014:40 Thursday, June 20, 2019
Location: 紫金大厅B(Grand Hall B)
Pete Warden (Google)
Average rating: *****
(5.00, 2 ratings)
Pete Warden explains how to use Google's open source framework to run machine learning models on embedded processors like microcontrollers and DSPs. Discover what you need to get started using the code itself, including hardware, coding tools, and getting the library built. 了解更多信息.
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14:0014:40 Thursday, June 20, 2019
Location: 多功能厅2(Function Room 2)
Arun Verma (Bloomberg)
Average rating: ****.
(4.00, 1 rating)
To gain an edge in the markets, quantitative hedge fund managers require automated processing to quickly extract actionable information from unstructured and increasingly nontraditional sources of data. Arun Verma details AI and machine learning (ML) techniques in quantitative finance that lead to profitable trading strategies. 了解更多信息.
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14:5015:30 Thursday, June 20, 2019
Location: 紫金大厅B(Grand Hall B)
Mark Ryan (IBM), Alina Zhang (Skylinerunners)
Average rating: *****
(5.00, 1 rating)
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. Learn about applying deep learning time series forecasting to machine learning as Mark Ryan and Alina Li Zhang explain how streetcar delays can be predicted...and prevented. 了解更多信息.
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14:5015:30 Thursday, June 20, 2019
Location: 多功能厅2(Function Room 2)
Chenta Lee (IBM)
By combining various analytics including DGA, squatting, tunneling, and rebinding detection, it's possible to build a DNS analytic playbook to anneal actionable threat intelligence from billions of DNS requests. Chenta Lee outlines how DNS volumetric data and analytics complement each other to create a new dimension to look at security postures and how to leverage it in security operations. 了解更多信息.
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16:2017:00 Thursday, June 20, 2019
Location: 紫金大厅B(Grand Hall B)
David Maman (Binah)
Average rating: *****
(5.00, 1 rating)
Zero-day attacks. IoT-based botnets. Cybercriminal AI versus cyberdefender AI. While these won’t be going away, they aren’t our biggest worry in cybercrime. Hacking humans is. David Maman demonstrates how the combination of minutes of video, signal processing, remote heart-rate monitoring, AI, ML, and data science can identify a person’s health vulnerabilities, which evildoers can make worse. 了解更多信息.
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16:2017:00 Thursday, June 20, 2019
Location: 多功能厅2(Function Room 2)
Maulik Soneji (GO-JEK), Jewel James (Gojek)
Hear how Maulik Soneji and Jewel James prototyped the search framework that personalizes the restaurant search results by using machine learning (ML) to learn what constitutes a relevant restaurant given a user's purchasing history. 了解更多信息.
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08:5009:00 Friday, June 21, 2019
Location: 紫金大厅A(Grand Hall A)
Abigail Hing Wen (Intel Corp.)
Abigail Hing Wen catches you up on some of the most exciting recent breakthroughs in the industry, including natural language processing strong enough to generate sentences indistinguishable from a human’s, highly accurate 3D protein structure prediction to fight disease, and leaps forward in reinforcement learning, a more natural but very complex alternative to other forms of machine learning. 了解更多信息.
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09:0009:15 Friday, June 21, 2019
Location: 紫金大厅A(Grand Hall A)
Haoyuan Li (Alluxio)
Haoyuan Li offers an overview of a data orchestration layer that provides a unified data access and caching layer for single cloud, hybrid, and multicloud deployments. It enables distributed compute engines like Presto, TensorFlow, and PyTorch to transparently access data from various storage systems while actively leveraging an in-memory cache to accelerate data access. 了解更多信息.
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09:3009:45 Friday, June 21, 2019
Location: 紫金大厅A(Grand Hall A)
Mikio Braun (Zalando)
What do your customers want? What are the current and upcoming trends? Mikio Braun takes a look at Zalando and the retail industry to explore how AI is redefining the way ecommerce sites interact with customers to create a personalized experience that strives to make sure customers find what they want when they need it. 了解更多信息.
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09:4510:00 Friday, June 21, 2019
Location: 紫金大厅A(Grand Hall A)
Yangqing Jia (Alibaba Group)
Average rating: *****
(5.00, 2 ratings)
The recent years of AI has grown out of labs and created a transformative power for a vast range of industries. But, while we take it for granted that AI and Cloud come hand in hand, I'll show you an argument one step further: AI should be Cloud Native. 了解更多信息.
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10:0010:20 Friday, June 21, 2019
Location: 紫金大厅A(Grand Hall A)
Michael James (Cerebras)
Average rating: ****.
(4.00, 1 rating)
Artificial intelligence is defining a new generation of computer technology with applications that blur the boundaries between intuition, art, and science. Michael James examines the fundamental drivers of computer technology, surveys the landscape of AI hardware solutions, and explores the limits of what's possible as new computer platforms emerge. 了解更多信息.
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10:2010:40 Friday, June 21, 2019
Location: 紫金大厅A(Grand Hall A)
Tim Kraska (MIT)
Average rating: *****
(5.00, 1 rating)
Systems and applications are composed from basic data structures and algorithms. Most of these have been around since the beginnings of CS and form every intro lecture. Yet, we might soon face an inflection point. Tim Kraska outlines different ways to build learned algorithms and data structures to achieve instance optimality and unprecedented performance for a wide range of applications. 了解更多信息.
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11:1511:55 Friday, June 21, 2019
Location: 紫金大厅B(Grand Hall B)
Joseph Spisak (Facebook)
Average rating: *****
(5.00, 1 rating)
Learn how PyTorch 1.0 enables you to take state-of-the-art research and deploy it quickly at scale in areas from autonomous vehicles to medical imaging. Joseph Spisak dives deep on the latest updates to the PyTorch framework including TorchScript and the JIT compiler, deployment support, and the C++ interface, and explains how Facebook uses PyTorch 1.0 to power AI across its products. 了解更多信息.
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11:1511:55 Friday, June 21, 2019
Location: 多功能厅2(Function Room 2)
Roger Chen (Computable)
Roger Chen details how to enable powerful data lineage properties with decentralized data governance models using blockchain technology. As a result, organizations can easily satisfy growing compliance regulations around data privacy while gaining access to rich external data resources for building AI models. 了解更多信息.
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11:5513:10 Friday, June 21, 2019
Location: 彩虹厅 (Rainbow Room)
与北京人工智能大会主题演讲讲师坐下来谈。午餐时间加入某一圆桌有机会就主题演讲面对面讨论,提问。座位有限。 Sit down with an AI Beijing keynote speaker. Drop in to a table at lunch for the chance to meet face-to-face and ask questions or discuss their keynote. Seating is limited. 了解更多信息.
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13:1013:50 Friday, June 21, 2019
Location: 紫金大厅B(Grand Hall B)
Yue Cathy Chang (TutumGene)
Genome editing has been dubbed a top technology that could create trillion-dollar markets. Learn how recent advancements in the application of AI to genomic editing are accelerating transformation of medicine with Yue Cathy Chang as she explores how AI is applied to genome sequencing and editing, the potential to correct mutations, and questions on using genome editing to optimize human health. 了解更多信息.
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13:1013:50 Friday, June 21, 2019
Location: 多功能厅2(Function Room 2)
Mikio Braun (Zalando)
Mikio Braun takes you through the past 20 years of machine learning research to explore aspects of artificial intelligence, then examines current examples like autonomous cars and chatbots. Together you'll put together a mental model for a reference architecture for artificial intelligence systems. 了解更多信息.
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14:0014:40 Friday, June 21, 2019
Location: 紫金大厅B(Grand Hall B)
Kaz Sato (Google)
Average rating: *****
(5.00, 1 rating)
Kaz Sato explains how creating an ML model is just a starting point. To bring the technology into production service, you need to solve various real-world issues such as building a data pipeline for continuous training, automated validation of the model, version control of the model, scalable serving infra, and ongoing operation of the ML infra with monitoring and alerting. 了解更多信息.
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14:5015:30 Friday, June 21, 2019
Location: 多功能厅2(Function Room 2)
AI pipelines simplify the lifecycle workflow management and enhance reproducibility and collaboration for machine learning and deep learning projects. Cloud native platform solutions offer great portability and scalability. Weiqiang Zhuang and Huaxin Gao show how, by combining strengths, AI pipelines on container platforms can help accelerate AI application development and deployment. 了解更多信息.
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16:2017:00 Friday, June 21, 2019
Location: 多功能厅2(Function Room 2)
Le Zhang (Microsoft), Jianxun Lian (Microsoft)
Enterprises benefit from recommendation systems for revenue and customer engagement, but creating such a system is time-consuming. Le Zhang and Jianxun Lian explore the Microsoft/Recommenders repository, which offers solutions to building recommendation systems. It contains classic and state-of-the-art algorithms from Microsoft and enables enterprise success by leveraging Azure's cloud capability. 了解更多信息.