The search engine has been a great platform for machine learning technologies, and the latest developments in AI open a new frontier, transforming the search engine into an AI platform. Hua Yang explores the deep learning and natural language understanding technologies used in eBay's ecommerce search platform.
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.
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.
人工智能对商业及社会的影响 (Impact of AI on Business and Society),
企业人工智能 (AI in the Enterprise)
Yi Zhang (University of California, Santa Cruz | Rulai)
Yi Zhang offers a comprehensive overview of the technology landscape of the chatbot. You’ll learn best practices with regard to evaluating technologies, how to assemble the right team to manage the process, user-centered bot design principles, and risk management. Along the way, Yi share bot use cases within several industries.
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.
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.
The relational database enabled the rise of BI systems, and NoSQL databases enabled web scale applications. Now, the future is cognitive computing. However, these systems process data that is more complex than before. Haikal Pribadi reviews the evolution of databases and explains where knowledge graphs and bases sit in this evolution. Could they serve as the next generation of databases?
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.
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 offers 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.
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.
To achieve high accuracy when reasoning about text, you generally need to understand specific languages, jargons, domain-specific documents, and writing styles. David Talby explains how to train custom word embeddings, named entity recognition, and question-answering models on the NLP library for Apache Spark.
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.
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.
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.
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.
Shyam Sundar explains how to use unsupervised machine learning to keep websites and mobile apps running smoothly under the stress of massive numbers such as those seen on Singles Day. With this method, pricing errors, conversion problems, and business opportunities can be caught early and resolved, protecting companies against revenue loss and brand damage.
Emmanuel Ameisen and Jeremy Karnowski 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.
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 properties, which can be further used to design and motivate new models, and how to reduce the computational complexity required to perform inference and learning in sophisticated models using graphical models.
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.
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.
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 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.
The next frontier in AI is transfer learning, which enables computers to apply what they’ve learned in one scenario to new situations, making AI-based systems far more powerful, reusable, and flexible. But is it ready for enterprise deployment, and if so, how can it be applied to solve business problems? Join Catherine Havasi to find out.
It is critical to analyze the business impact on finance market from worldwide events. 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.
Since its first release in May of 2014, more than 100 million users in China, Japan, and the US have interacted with renowned AI product Xiaoice (小冰), which builds human-like conversation. Li Zhou shares key lessons learned from the past four years and explains how to use them to build a better chatbot experience.
近两年外卖行业发展迅速，美团外卖每日超过1600万订单，线下有50万名骑手每天奔波在大街小巷进行配送，是全球最大的外卖平台。如何使数据巨大的骑手配送得更有效率，减少空驶？如何让用户更早地享受到美食，减少超时率？这是一个强随机环境下的大规模复杂优化问题。本次分享将介绍美团配送在运用大数据、机器学习和运筹优化技术解决即时配送业务难题、利用 AI 技术来取代人工上的若干进展和探索，帮助大家了解这一技术领域的进展和挑战。
以深度学习为代表的人工智能技术通常需要大量的有标签训练数据，这对于很多应用领域而言并非易事。为了解决这个挑战，我们利用人工智能的对称之美——很多人工智能任务天然就是双向的，比如中到英翻译 vs.英到中翻译，图像分类 vs. 图像生成，语音识别 vs. 语音合成——来为机器学习建立闭环、生成有效的反馈信号，从而在缺乏有标签数据的情况下也能实现高效学习。我们将这种新型的学习方法称之为“对偶学习”。对偶学习已经被成功应用到诸多领域，取得了非同凡响的效果。本报告中，我们将针对对偶学习的数学模型、优化算法、概率解释、实验结果，收敛性分析等进行详细讨论，展示对偶学习的魅力，并对它在人工智能领域的更广泛应用进行展望。对偶学习有关的研究成果已发表在NIPS、ICML、IJCAI、AAAI等人工智能领域最顶尖的国际会议之上。