Summer 2021: IAAI

Vol 42 No 2: Summer 2020 | Published: 2022-01-11

Editorial Introduction

Ruchir Puri, Neil Yorke-Smith

This special issue presents nine articles that are comprehensive case studies of deployed applications, carefully selected out of the breadth of papers from the 32nd Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-20). IAAI is the premier conference for applied AI research, providing a forum for disseminating work on novel uses of AI technology — ranging from the insightful application of existing techniques to previously unexplored domains through to new or enhanced AI methods that show significant impact for applications considered previously.

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Deploying an Artificial Intelligence-Based Defect Finder for Manufacturing Quality Management

Kyoung Jun Lee, Jun Woo Kwon, Soohong Min, Jungho Yoon

This paper describes how the Big Data Research Center of Kyung Hee University and Benple Inc. developed and deployed an artificial intelligence system to automate the quality management process for Frontec, an SME company that manufactures automobile parts. Various constraints, such as response time requirements and the limited computing resources available, needed to be considered in this project. Defect finders using large-scale images are expected to classify weld nuts within 0.2 s with an accuracy rate of over 95%. Our system uses Circular Hough Transform for preprocessing as well as an adjusted VGG (Visual Geometry Group) model. Our convolutional neural network (CNN) system shows an accuracy of over 99% and a response time of about 0.14 s. To embed the CNN model into the factory, we reimplemented the preprocessing modules using LabVIEW and had the classification model server communicate with an existing vision inspector. We share our lessons from this experience by explain-ing the procedure and real-world issues developing and embedding a deep learn-ing framework in an existing manufacturing environment without implementing any hardware changes.

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Federated Learning-Powered Visual Object Detection for Safety Monitoring

Yang Liu, Anbu Huang, Yun Luo, He Huang, Youzhi Liu, Yuanyuan Chen, Lican Feng, Tianjian Chen, Han Yu, Qiang Yang

Visual object detection is an important artificial intelligence (AI) technique for safety monitoring applications. Current approaches for building visual object detection models require large and well-labeled dataset stored by a centralized entity. This not only poses privacy concerns under the General Data Protection Regulation (GDPR), but also incurs large transmission and storage overhead. Federated learning (FL) is a promising machine learning paradigm to address these challenges. In this paper, we report on FedVision—a machine learning engineering platform to support the development of federated learning powered computer vision applications—to bridge this important gap. The platform has been deployed through collaboration between WeBank and Extreme Vision to help customers develop computer vision-based safety monitoring solutions in smart city applications. Through actual usage, it has demonstrated significant efficiency improvement and cost reduction while fulfilling privacy-preservation requirements (e.g., reducing communication overhead for one company by 50 fold and saving close to 40,000RMB of network cost per annum). To the best of our knowledge, this is the first practical application of FL in computer vision-based tasks.

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Optimizing Smart Grid Operations from the Demand Side

Yongqing Zheng, Han Yu, Yuliang Shi, Kun Zhang, Shuai Zhen, Lizhen Cui, Cyril Leung, Chunyan Miao

As demand for electricity grows in China, the existing power grid is coming under increasing pressure. Expansion of power generation and delivery capacities across the country requires years of planning and construction. In the meantime, to ensure safe operation of the power grid, it is important to coordinate and optimize the demand side usage. In this paper, we report on our experience deploying an artificial intelligence (AI)–empowered demand-side management platform – the Power Intelligent Decision Support (PIDS) platform – in Shandong Province, China. It consists of three main components: 1) short-term power consumption gap prediction, 2) fine-grained Demand Response (DR) with optimal power adjustment planning, and 3) Orderly Power Utilization (OPU) recommendations to ensure stable operation while minimizing power disruptions and improving fair treatment of participating companies. PIDS has been deployed since August 2018. It is helping over 400 companies optimize their power usage through DR, while dynamically managing the OPU process for around 10,000 companies. Compared to the previous system, power outage under PIDS due to forced shutdown has been reduced from 16% to 0.56%.

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Day-Ahead Forecasting of Losses in the Distribution Network

Nisha Dalal, Martin Mølna, Mette Herrem, Magne Røen, Odd Erik Gundersen

Utility companies in the Nordics have to nominate how much electricity is expected to be lost in their power grid the next day. We present a commercially deployed machine learning system that automates this day-ahead nomination of the expected grid loss. It meets several practical constraints and issues related to, among other things, delayed, missing and incorrect data and a small data set. The system incorporates a total of 24 different models that performs forecasts for three sub-grids. Each day one model is selected for making the hourly day-ahead forecasts for each sub-grid. The deployed system reduced the mean average percentage error (MAPE) with 40% from 12.17 to 7.26 per hour from mid-July to mid-October, 2019. It is robust, flexible and reduces manual work. Recently, the system was deployed to forecast and nominate grid losses for two new grids belonging to a new customer. As the presented system is modular and adaptive, the integration was quick and needed minimal work. We have shared the grid loss data-set on Kaggle.

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Improving Search Engine Efficiency through Contextual Factor Selection

Anxiang Zeng, Han Yu, Qing Da, Yusen Zhan, Yang Yu, Jingren Zhou, Chunyan Miao

Learning to rank (LTR) is an important artificial intelligence (AI) approach supporting the operation of many search engines. In large-scale search systems, the ranking results are continually improved with the introduction of more factors to be considered by LTR. However, the more factors being considered, the more computation resources required, which in turn, results in increased system response latency. Therefore, removing redundant factors can significantly improve search engine efficiency. In this paper, we report on our experience incorporating our Contextual Factor Selection (CFS) deep reinforcement learning approach into the Taobao e-commerce platform to optimize the selection of factors based on the context of each search query to simultaneously maintaining search result quality while significantly reducing latency. Online deployment on Taobao.com demonstrated that CFS is able to reduce average search latency under everyday use scenarios by more than 40% compared to the previous approach with comparable search result quality. Under peak usage during the Single’s Day Shopping Festival (November 11th) in 2017, CFS reduced the average search latency by 20% compared to the previous approach.

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Clarity 2.0: Improved Assessment of Product Competitiveness from Online Content

Yufeng Huang, Mariana Bernagozzi, Michelle Morales, Sheema Usmani, Biplav Srivastava, Michelle Mullins

Competitive analysis is a critical part of any business. Product managers, sellers, and marketers spend time and resources scouring through an immense amount of online and offline content, aiming to discover what their competitors are doing in the marketplace to understand what type of threat they pose to their business’ financial well-being. Currently, this process is time and labor-intensive, slow and costly. This paper presents Clarity, a data-driven unsupervised system for assessment of products, which is currently in deployment in the global technology company, IBM. Clarity has been running for more than a year and is used by over 4,500 people to perform over 200 competitive analyses involving over 1000 products. The system considers multiple factors from a collection of online content: numeric ratings by online users, sentiment of user generated online content for key product performance dimensions, content volume, and topic analysis of content. The results and explanations of factors leading to the results are visualized in an interactive dashboard that allows users to track their product’s performance as well as understand main contributing factors. Its efficacy has been tested in a series of cases across IBM’s portfolio which spans software, hardware, and services. After initial release and first year of use, improvements to the methodology were implemented to ensure it was relevant to and served the highest impact needs of target users. Moreover, new use cases leveraging the initial ideas and approaches continue to be explored.

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Considerations on Creating Conversational Agents For Multiple Environments and Users

Advances in artificial intelligence algorithms and expansion of straightforward cloud-based platforms have enabled the adoption of conversational assistants by both, medium and large companies, to facilitate interaction between clients and employees. The interactions are possible through the use of ubiquitous devices (e.g., Amazon Echo, Apple HomePod, Google Nest), virtual assistants (e.g., Apple Siri, Google Assistant, Samsung Bixby, or Microsoft Cortana), chat windows on the corporate website, or social network applications (e.g. Facebook Messenger, Telegram, Slack, WeChat). Creating a useful, personalized conversational agent that is also robust and popular is nonetheless challenging work. It requires picking the right algorithm, framework, and/or communication channel, but perhaps more importantly, consideration of the specific task, user needs, environment, available training data, budget, and a thoughtful design. In this paper, we will consider the elements necessary to create a conversational agent for different types of users, environments, and tasks. The elements will account for the limited amount of data available for specific tasks within a company and for non-English languages. We are confident that we can provide a useful resource for the new practitioner developing an agent. We can point out novice problems/traps to avoid, create consciousness that the development of the technology is achievable despite comprehensive and significant challenges, and raise awareness about different ethical issues that may be associated with this technology. We have compiled our experience with deploying conversational systems for daily use in multicultural, multilingual, and intergenerational settings. Additionally, we will give insight on how to scale the proposed solutions.

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Rethinking AI Magazine Again

Ashok Goel

I have been affiliated with AI Magazine for a long time. In 2010, David Leake, the then Editor-in-Chief of AIM, invited me to join the magazine’s editorial board. About five years later, David and Mike Hamilton, AIM’s Managing Editor, invited me to become the magazine’s next Editor-in-Chief. After serving as an Associate Editor for about a year and receiving approval from AAAI’s Executive Council, I became the Editor-in-Chief of AI Magazine on August 1, 2016. About that time, I wrote an editorial titled Rethinking AI Magazine (Winter 2016, pp. 3-4). Now, almost five years later, as I step down as the AIM’s Editor-in-Chief, I want to revisit the vision articulated in that editorial.

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