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

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)
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. 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 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. 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)
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. 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)
Transfer Learning has been proven to be a tremendous success in the Computer Vision field as a result of ImageNet competition. In the past months, the Natural Language Processing field has witnessed several breakthroughs with transfer learning, namely ELMo, Transformer, ULMFit and BERT. In this talk, David will be showcasing the use of transfer learning on NLP application with SOTA accuracy. 了解更多信息.
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11:1511:55 Thursday, June 20, 2019
Location: 多功能厅6A+B (Function Room 6A+B)
Tao Lu (Microsoft), Chenhui Hu (Microsoft)
Forecasting customer activities is one of the most important and common business problems. In Microsoft Azure Identity team, we forecast customer behavior based on billions of user activities. We will share how we improve 25% of forecasting accuracy with dilated convolutional neural networks and reduce 80% of the time in development with the best practices of time series forecasting. 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)
Bichen Wu (UC Berkeley)
The success of deep neural networks is attributed to three factors: stronger computing capacity, more complex neural networks, and more data. These factors, however, are usually not available with the edge applications as autonomous driving, AR/VR, IoT, and so on. In this talk we discuss how we apply AutoML, SW/HW codesign, domain adaptation to solve these problems. 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 is the approach of training ML models across a fleet of participating devices, without collecting their data in a central location. Alex Ingerman introduces 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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14:5015:30 Thursday, June 20, 2019
Location: 紫金大厅B(Grand Hall B)
Mark Ryan (IBM), Alina Li Zhang (Skylinerunners)
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. Using deep learning and time-series forecasting, we'll show 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)
Arun Verma (Bloomberg)
We illustrate use of AI and ML techniques in Quantitative finance that lead to profitable trading strategies. Passive investing (or Quantamental investing) is now very popular and many techniques from deep learning, reinforcement learning as well as NLP and sentiment analysis are being used for a broad set of data sets such as News and Geolocational data. 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.ai)
Zero-day attacks. IoT-based botnets. Cybercriminal AI v. cyberdefender AI. While these won’t be going away, they aren’t the biggest worry we have in cybercrime. Hacking humans is. The combination of mere minutes of video, signal processing, remote heart rate monitoring, AI, machine learning, 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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11:1511:55 Friday, June 21, 2019
Location: 紫金大厅B(Grand Hall B)
Joseph Spisak (Facebook)
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. We'll deep dive on the latest updates to the PyTorch framework including TorchScript and the JIT compiler, deployment support, the C++ interface. We will also cover how PyTorch 1.0 is utilized at Facebook to power AI across a variety of products. 了解更多信息.
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11:1511:55 Friday, June 21, 2019
Location: 报告厅(Auditorium)
Yang Wang (Intel)
We will introduce Analytics Zoo, a unified analytics + AI platform for distributed TensorFlow, Keras and BigDL on Apache Spark, designed for production environment. It enables easy deployment, high performance and efficient model serving for deep learning applications. 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)
Sarah Aerni (Salesforce Einstein)
At Salesforce Einstein data science is an agile partner to over 100,000 customers. How do we achieve this scale? We share lessons learned in business, technology and process along the way. Via use cases, oft-missed foundational elements for deployment, and the evaluations that must happen along the way, we will share how to achieve and sustain models in production, and where to go from there. 了解更多信息.
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14:0014:40 Friday, June 21, 2019
Location: 多功能厅2(Function Room 2)
安敖日奇朗 (Rakuten, Inc.), TzuLin Chin (Rakuten, Inc.)
Data Science Platform is a suite of tools for exploring data, training models, and running GPU/CPU compute jobs in an isolated container environment. It provides one click machine learning environment creation, powerful job scheduler and flexible "function as a service" component. It runs on Kubernetes and supports both on-premises and cloud environment, as well as hybrid mode. 了解更多信息.
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14:5015:30 Friday, June 21, 2019
Location: 紫金大厅B(Grand Hall B)
Kaz Sato (Google)
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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16:2017:00 Friday, June 21, 2019
Location: 紫金大厅B(Grand Hall B)
Bas Geerdink (ING)
AI is at the core of ING’s business. We are a data-driven enterprise, with ‘analytics skills’ as a top strategic priority. We are investing in AI, big data, and analytics to improve business processes such as balance forecasting, fraud detection and customer relation management. In this talk, Bas will give an overview of the use cases and technology to inspire the audience! 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)
Maulik Soneji (Go-jek), Jewel James (Go-jek)
The story of how we prototyped the search framework that personalizes the restaurant search results by using ML to learn what constitutes a relevant restaurant given a user's purchasing history 了解更多信息.