A practical guide toward 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.
Design thinking for AI
Chris Butler (Philosophie)
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.
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.
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)
Lu Zhang (Microsoft)
Henry Zeng (Microsoft)
xiao zhang (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!
Intel OpenVINO：加速从边缘到云端的深度学习的推断和计算机视觉(Intel OpenVINO: Accelerating deep learning inference and computer vision from edge to cloud)
Zhen Zhao (Intel)
How Intel OpenVINO provides highly optimized cross-platform Deep learning deployment and visual AI solution based on various Intel architectures. And the structure and workflow of Intel OpenVINO™ toolkit, optimization methods by Asynchronies & heterogeneous computing, low precision inference, instruction set acceleration.
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.
Analytics Zoo：基于Apache Spark的分布式TensorFlow和Keras(Analytics Zoo: Distributed TensorFlow and Keras on Apache Spark)
Zhichao Li (Intel)
Kai Huang (Intel)
Yang Wang (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.)
Forecasting customer activities with dilated convolution neural networks: Use case and best practices
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.
AI at ING: The why, how, and what of a data-driven enterprise
Bas Geerdink (ING)
AI is at the core of ING’s business. It is a data-driven enterprise, with analytics skills as a top strategic priority, and 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 use cases and technology.
TensorFlow lite for microcontrollers
Pete Warden (Google)
Pete Warden explores how you can 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.
Using deep learning and time-series forecasting to reduce transit delays
Mark Ryan (IBM),
Alina Li Zhang (Nobul)
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.
Hacking humans made easy: Signal processing + AI + video
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.
ONNX:开放和互操作平台让AI无处不在(AI everywhere: Open and interoperable platform for AI with ONNX)
Henry Zeng (Microsoft)
Klein Hu (Microsoft)
Emma Ning (Microsoft)
An open and interoperable ecosystem enables you to choose the framework that's right for you, train at scale, and deploy to cloud and edge. ONNX provides a common format supported by many popular frameworks and hardware accelerators. This session provides an introduction to ONNX and its core concepts. The session will be delivered in English and Chinese jointly.
自动机器学习（Automated machine learning）技术的实践与应用
Hui Xue (微软亚洲研究院)
人工智能在过去的几年里飞速发展，但是机器学习的实践和应用需要消耗一定的人力和时间。例如，如何去做特征选择，如何设计一个适合该任务的神经网络模型等等。而自动机器学习技术，可以帮助开发者和机器学习实战者，缩短开发周期，提高效率。我们的介绍主要包括：自动机器学习技术的进展；我们开源的自动机器学习开源库Neural Network Intelligence; 如何利用自动机器学习的技术，在产品和应用上提高效率，节省所需的时间和缩短周期。我们会在最后一部分，分享一些利用自动特征选择，自动参数调整以及模型架构搜索上的成功案例。
令人兴奋的TensorFlow 2.0新功能(Exciting new features in TensorFlow 2.0)
Tiezhen Wang (Google)
TensorFlow 2.0 is a major milestone with a focus on ease of use. This talk will give a in depth introduction to the new exciting features and best practices. Topics such as distributed strategies and edge deployment (TensorFlow Lite and TensorFlow.js) will also be covered.
The unreasonable effectiveness of transfer learning on natural language processing
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.
The future of machine learning is decentralized
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.
Trading strategies using alternative data and machine learning
Arun Verma (Bloomberg)
Arun Verma illustrates the use of AI and ML techniques in quantitative finance that leads to profitable trading strategies. Passive investing (or quantamental investing) is now very popular and many techniques from deep learning and reinforcement learning as well as NLP and sentiment analysis are being used for a broad set of datasets such as news and geolocational data.
Detect the Undetectable at the Breach
Chenta Lee (IBM)
By combining various analytics including DGA, squatting, tunneling, and rebinding detection, we built a DNS analytic playbook to anneal actionable threat intelligence from billions of DNS requests. We will show how DNS volumetric data and analytics complement each other to create an new dimension to look at security postures. Moreover, how to leverage it in security operations?
Using ML for personalizing food recommendations
Maulik Soneji (Go-jek)
Jewel James (Go-jek)
Hear the story of 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.
Enlighten the future of games with artificial intelligence
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.
一个改善债务催收的AI解决方案(A humane AI solution to improve debt collection)
Ying Liu (Abakus 鲸算科技(Wecash闪银）)
AI debt collection platform of Abakus provides a friendly and humane product solution which is designed for people who work in the live agents of the organization in the frontline. The agent training of the organization could be enhanced more smoothly with an AI friendly culture. It has been proved in our experiment that the performance of the collection assistants has been highly improved.
Office Depot利用基于Apache Spark的深度学习实现实时产品推荐(Real-time product recommendations leveraging deep learning on Apache Spark in Office Depot)
Kai Huang (Intel)
Real-time recommender systems are critical for the success of the ecommerce industry. Join Kai Huang, Luyang Wang, and Jing Kong as they showcase how to build efficient recommender systems for the ecommerce industry using deep learning technologies.
AI如何彻底改变风电行业(How AI is revolutionizing the wind power industry)
Dongfeng Chen (Clobotics)
One of the biggest challenges to growth remains the high costs of constructing wind farms, as well as the ongoing operations and maintenance costs. Dongfeng Chen dives into the successes and failures of creating an entirely autonomous visual-recognition-powered drone inspection solution for turbine blades, which increased the efficiency by 10 times.
Min Shen (LinkedIn)
领英公司的几乎所有产品都离不开基于海量数据和大规模数据运算的机器学习模型。怎样构建一个稳定，高效，和易用的人工智能基础架构，越来越成为一个核心的问题。 这个演讲会先介绍领英大数据团队是怎样在5年的时间里演进这个基础架构，从开始的完全基于Spark的系统，到现在Spark+TensorFlow的环境。 我们还会重点介绍近期解决的技术挑战，来应对接近500PB数据和将近6亿会员的巨大经济图谱。这些挑战包括大规模重量级的深度学习模型，Spark的调优，以及在机器学习生产线中连接不同的步骤（数据准备，模型构建，模型训练，在线inference)。 最后我们会介绍我们近期一些成功的深度学习案例，以及团队在AI基础架构上未来2～3年的计划和愿景。
peng ni (凡普金科集团有限公司)
该议题主要包括：1. 语音切分技术的原理和应用；2. 语音识别模型的构建优化；3. 语音情感分析构建应用；4. 语音数据的实时处理框架；5. 金融场景业务落地。
Mingxi Wu (TigerGraph)
图数据上的非监督学习在激活大数据的经济价值上有着广泛和不可替代的作用。 PageRank能够发掘重要的实体, 社区发掘(community detection)可以找到具有某种特性的群体，紧密度中心性算法(Closeness Centrality)可以自动找到远离群体的个体。所有这些算法都是非监督的学习。 我们分享一些具体客户案例来展示他们的价值，同时分享怎样在大数据上灵活应用这些开源算法。
Squirrel AI Learning的AI导师：AI-adaptive技术在K-12教育中的实际应用(Squirrel AI Learning’s AI tutors: Real-life applications of AI-adaptive technology in K–12 education)
Xing Fan (Squirrel AI)
Squirrel AI Learning is the first artificial intelligence technology company in China to apply AI-adaptive technology to K–12 education. Xing Fan dives deep into its implementation approach and teaches you about the business process, pedagogy, architecture, operation, and theoretical underpinning of this adaptive learning service.
A fresh approach to building high-performance AI systems (sponsored by Habana Labs)
Eitan Medina (Habana Labs)
The new class of purpose-built AI processors presents datacenter 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.
基于数据中心基础架构的深度学习（由Dell Technologies赞助）(A deep learning harness built on data center infrastructure (sponsored by Dell Technologies))
Improve the utilization rate of data center resources. Join in to explore DL infrastructure and a GPU-as-a-service solution. You'll learn how it simplifies the AI compute requirements with automated access, better control, and simplified provisioning all while pushing your GPU resources to the limit accelerating your model training and inference.
创邻Galaxybase图数据库和AI应用（由创邻科技赞助）(The world's fastest graph database Galaxybase and AI applications (sponsored by Chuang Lin Tech)
Chen Zhang (Chuang Lin Tech)
基于Spark使用AI来玩游戏(Game playing using AI on Spark)
Shan Yu (Intel)
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 takes you through her experiences from her attempts in using the AI on Spark for playing games.
统一大数据分析和人工智能从而更快地大规模洞察(Unifying analytics and AI on big data for faster insights at scale)
马子雅 (Ziya Ma) (Intel)
Ziya Ma walks you through Intel’s scalable data insights strategy and related big data analytics and AI technologies such as Analytics Zoo—an end-to-end analytics and AI pipeline for developing full solutions with Apache Spark on Intel Xeon and Intel Optane DC Persistent Memory at scale. She highlights customers use cases and collaboration with industry leaders throughout.
解锁数据的力量; 拥抱智能+（由Dell Technologies赞助）(Unlock the power of data; embrace intelligent+ (sponsored by Dell Technologies))
Frank Wu (Dell)
This is the data era. Data helps to make better products and services, allowing a company to attract more customers, which results in more data—and repeat. Eventually, this turns into data capital, the most valuable corporate asset. Frank Wu explains why how you use your data will determine your future.
云服务加速人工智能创新（Accelerate innovations with AI in the cloud）
Long Wang (Tencent)
We all know that Cloud is the best place to use new technologies. Long Wang examines what's happening for AI in the cloud. How does AI in the cloud accelerate the innovations in the industry? What's mostly possible? What's still on the way? How does cloud help?
Increasing AI Productivity and Efficiency with Purpose-built AI Processors (Sponsored by Habana Labs)
Eitan Medina (Habana Labs)
Mr. Medina will discuss advances made possible with AI Processors designed to address AI-specific computing requirements, chief among them being increasing AI throughput speeds, while lowering power consumption. This new class of AI processing brings significantly improved productivity and efficiency to the datacenter to overcome limitations of existing CPU- and GPU-based solutions.
The future of hiring and the talent market with AI
Maria Zhang (LinkedIn)
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.
The Future of Machine Learning is Tiny
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 it can be implemented on existing chips, and some of the new uses it will unlock.
AI and Systems at RISELab
Ion Stoica (UC Berkeley)
In this talk, I will describe a few projects at the intersection of AI and Systems that we are developing at RISELab, UC Berkeley. The RISELab is the successor of AMPLab, where several highly successful open source projects, including Apache Spark and Apache Mesos, were developed.
Bringing research and production together with PyTorch 1.0
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.
Artificial intelligence meets genomics: accelerating understanding of our genetic make up and use of genome editing to revolutionize medicine
Yue Cathy Chang (TutumGene)
Genome editing has been dubbed as a top technology that could create trillion-dollar markets in the next decade. Recent advancements in the application of AI to genomic editing are accelerating transformation of medicine. We will discuss how AI is applied to genome sequencing, genome editing and their potential to correct mutations, and questions on using genome editing to optimize human health.
Deep prediction: A year in review for deep learning for time series
Aileen Nielsen (Skillman Consulting)
Deep learning for time series analysis has made rapid progress in 2018 and 2019, with advances in the use of both convolutional and recurrent neural network architectures. The state of the art in deep forecasting will be summarized for 2018 and 2019, including use cases in both forecasting and generating time series.
ML ops and Kubeflow pipeline
Kazunori 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.
Analytics Zoo：基于Apache Spark的生产级别分布式TensorFlow(Analytics Zoo: Distributed TensorFlow in production on Apache Spark)
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.
Sparkling: 基于Apache Spark进行一站式机器学习
Yiheng Wang (Tencent)
机器学习项目在企业中实际落地往往涉及到复杂工作流构建和数据管理，以及多种工具的整合。而且随着数据规模的增加，团队规模的扩大，这一任务更具挑战性。Apache Spark是业界流行的大数据框架，被广泛的应用在海量数据的分析处理。本议题将介绍我们在腾讯云上如何基于Apache Spark为客户建立一个一站式机器学习平台的相关工作。主要内容包括多种数据源的接入，构建复杂数据管线，利用数据可视化理解数据，通过可插拔的机制使用各种流行的机器学习框架，以及部署和监控模型。我们也会分享在这一过程中遇到的问题和挑战。听众也可以了解到，通过这种和大数据紧密结合的一站式机器学习，用户可以怎样更加高效的建立和管理他们的机器学习项目，从而加速了机器学习在业务中的落地。
AVA：Qiniu的云原生深度学习平台(AVA: A cloud native deep learning platform at Qiniu)
Chaoguang Li (Qiniu)
Bin Fan (Alluxio)
Haoyuan Li (Alluxio)
Atlab Lab at Qiniu Cloud focuses on deep learning for computer vision. Our team has built a high-performance and cost-effective training platform based on Cloud for deep learning, called AVA, which deeply integrates open source software stack including Tensorflow, Caffe, Alluxio and KODO our own cloud object storage.
查询地球：Uber的地理空间大数据分析(Query the planet: Geospatial big data analytics at Uber)
Zhenxiao Luo (Uber)
One of the distinct challenges for Uber is analyzing geospatial big data. Locations and trips provide insights that can improve business decisions and better serve users. Geospatial data analysis is particularly challenging, especially in a big data scenario. For these analytical requests, we must achieve efficiency, usability, and scalability in order to meet user needs and business requirements.
Decentralized governance of data
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.
Architecting AI applications
Mikio Braun (Zalando SE)
Mikio Braun looks back on the past 20 years of machine learning research to explore aspects of artificial intelligence. He then turns to current examples like autonomous cars and chatbots, putting together a mental model for a reference architecture for artificial intelligence systems.
Best practice of building data science platform in Rakuten
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.
AI pipelines on container platform
WEIQIANG ZHUANG (IBM)
Huaxin Gao (IBM)
AI pipelines simplifies the lifecycle workflow management and enhances the reproducibility and collaboration for machine learning/deep learning. A cloud native platform solution is great at portability and scalability. Combining both strengths, AI pipelines on container platform can help accelerate both AI applications development and deployment.
Best Practices for Building Enterprise-grade Recommendation Systems on Azure with Microsoft/Recommenders
Le Zhang (Microsoft), Jianxun Lian (Microsoft)
Enterprises benefit from recommendation system for revenue and customer engagement. Creating such a system and putting it to operation is time consuming. Microsoft/Recommenders repository offers solutions to facilitate building recommendation systems. It contains classic algorithms and state-of-the-arts from Microsoft, and enables enterprise success by leveraging Azure cloud capability.
Li Yuan (Perceptin 深圳普思英察科技有限公司)
中国电信如何利用对抗性自动编码器来对抗金融诈骗(How China Telecom combats financial fraud with adversarial autoencoders)
Weisheng Xie (China Telecom BestPay Co., Ltd)
We exploit the good representation capability of AAE (Adversarial AutoEncoder) in our risk factors modeling in fighting a special kind of financial frauds. It's one step of our long stack of unsupervised tasks, yet it's proved to be efficient and effective in our practice.
Hongyu Cui (DataVisor)
对视频进行精彩度分析，有助于筛选优质内容，尤其是冷启动阶段 同时，基于算法对精彩内容的理解，可以辅助创作，如进行标题辅助生成、动态/精彩封面生成、智能拆条等 我们通过对视频、音频、文本等多模态内容分析，同时利用用户交互数据，建立了完备的视频精彩度分析系统，并落地在长/短视频的不同业务场景下，明显提升了业务产出质量和效率
基于知识图谱的可解释性推荐系统(Explainable reasoning over knowledge graphs for recommendation)
Dingxian Wang (eBay)
Incorporating knowledge graphs into recommender systems has attracted increasing attention in recent years. By exploring the interlinks within a knowledge graph, the connectivity between users and items can be discovered as paths. Dingxian Wang and Canran Xu explore their new model, knowledge-aware path recurrent network (KPRN), which can be used to exploit knowledge graphs for recommendation.
PAI张量加速器和优化器：又一个深度学习编译器(PAI tensor accelerator and optimizer: Yet another deep learning compiler)
本次演讲会介绍阿里计算平台PAI团队过去一年多时间里在深度学习编译器领域的技术工作进展----PAI TAO(Tensor Accelerator and Optimizer)。PAI-TAO采用通用编译优化技术，来解决PAI平台所承载的多样性AI workload面临的训练及推理需求的性能优化问题，在部分workload上获得了20%到4X不等的显著加速效果，并且基本作到用户层全透明，在显著提升平台效率性能的同时也有效照顾了用户的使用惯性。目前PAI-TAO已经先后用于支持阿里内部搜索、推荐、图像、文本等多个业务场景的日常训练及推理需求。
Intel架构的低精度推断(Low-precision inference on Intel architecture)
Lei Xia (Intel)
Vector Neural Network Instructions or VNNI is the new Intel instruction set for low precision AI inference inside next generation Xeon platform. This lecture is to introduce the features of the VNNI and Intel software tools to support developers to use this new instruction set to accelerate inference with INT8.
Top AI Breakthroughs You Need to Know
Abby Wen (Intel Corp.)
Abigail will catch 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.
Data Orchestration for AI, Big Data, and Cloud
Haoyuan Li (Alluxio)
In this presentation, we present a data orchestration layer that provides a unified data access and caching layer for single cloud, hybrid and multi-cloud deployments. It enables distributed compute engines like Presto, TensorFlow, and PyTorch to transparently access data from various storage systems while actively leveraging in-memory cache to accelerate data access.
自驾驶技术与未来自动化车辆仓到仓运输(Self-driving technology and the future autonomous depot-to-depot transport)
Hao Zheng (PlusAI)
Hao Zheng dives into how PlusAI is developing a full stack self-driving technology to enable large scale autonomous commercial fleets. Building an autonomous truck that's both safe and efficient presents unique challenges across different layers of the technology stack. Hao examines some of these challenges, along with how PlusAI is addressing them.
AI and retail
Mikio Braun (Zalando SE)
Taking a look at Zalando and the retail industry we will explore how AI is redefining the way e-commerce sites interact with the customer to create a personalized experience that strives to make sure customers will find what they want when they need it.
为什么说人工智能和云计算乃天作之合？（Why do we say AI Should be Cloud Native?）
Yangqing Jia (Facebook)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.
Designing Computer Hardware for Artificial Intelligence
Michael James (Cerebras)
Artificial Intelligence is defining a new generation of computer technology with applications that blur boundaries between intuition, art, and science. We will discuss the fundamental drivers of computer technology, survey the landscape of AI hardware solutions, and explore the limits of what is possible as new computer platforms emerge.
Toward learned algorithms, data structures, and systems
Tim Kraska (MIT)
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.