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No. 1 (December 2016) Special Issue on AI & Social Value Creation

Vol.11 No.1 (December, 2016)

This special issue introduces our activities in creating social value with NEC’s AI platforms, our vision for social solutions, and the broad range of AI technologies that support their realization. Also in special interview with an NEC researcher and his counterpart in a large-scale collaboration project pursued by NEC in the field of AI, you will get a peek at our vision of the technology that will drive our future solutions.

Special Issue on AI & Social Value Creation

Remarks for Special Issue on AI & Social Value Creation
EMURA Katsumi
Executive Vice President, CTO and
Member of the Board


Social Vision in the Age of AI − Work, life, and the pursuit of a new ethics −
Joichi Ito
Director
MIT Media Lab

As AI becomes ever more prevalent, one thing is certain: its impact on society will be tremendous. How will AI evolve? How will it change the way we work? And how will it affect the nature of our society? We asked Mr. Joichi Ito, the director of the MIT Media Lab, about his vision of the future.


NEC’s Vision for AI in Social Value Creation
YAMADA Akio
General Manager
Data Science Research Laboratories

For the past several years, the world has witnessed an explosion of activity in the domain of artificial intelligence that has been termed the 3rd AI boom. NEC has devoted over half of a century of research and development to the field of AI that has culminated in the development of NEC the WISE - an array of cutting technologies including machine recognition and natural language parsing. By examining the undercurrents that have driven our development of NEC the WISE including the concepts and directions for future AI research and development from the perspective of the three functions of AI: Recognition, Optimization and Reasoning, this article will introduce the reader to the expanded customer value and our approach to developing solutions to issues facing society that NEC aims to achieve through AI.


Creating new social value

Safety Operations Supporting the Security of Urban Locations
HIROAKI Toshihiko

New types of criminal activity such as home-grown terrorism are becoming difficult to prevent by the traditional monitoring methods that focus on a visual black list matching. In the future it is expected that the utilization of various AI technologies as well as white list matching will make it possible to detect the unsuspected anomalies that are hard to discover or predict by human endeavor alone. Thereby it will be possible to adopt measures that will even be able to deal with new types of crime. The system design is also anticipated to shift from crime prevention market to normal operation management market, so that even greater value can be created. This paper outlines the viewpoint of NEC regarding surveillance solutions in support of the safety and security of urban locations and discusses progress in creating technologies that will provide support for them.


The Retail Industry Offers New Experiences for Consumers
TAKEDA Naohiro, SAKAI Junji, MIYANO Hiroyoshi
MIYAKAWA Shinya, SOEJIMA Kenji

The retail industry is entering an era of major reforms. This is due to circumstances that include demographic changes brought about by the recent low birth rates and a trend toward increased longevity, changes in lifestyle and taste and rapid changes in the ICT environment. This paper introduces perspectives for reforming the procedures adopted in actual shops by using AI. These include loss reduction by visualization of persons and things in the shops, improvement of the efficiency of shop procedures by real-time identification of purchasing behaviors, support for shop management by AI and improved shop standards by IoT.


“NEC the WISE” for City Transportation
Mervyn Cheah

City transportation plays a significant part to the society in driving urban mobility and productivity that realize smart cities vision. The program by “NEC the WISE” for transportation assists cities such as Singapore in achieving smooth and convenient travels for commuters. The objective is to build smarter management and optimization systems for trains, buses, taxis as well as smarter traffic management system to ensure that commuters get from one place to another in the fastest and most convenient way. This can be achieved using AI to perform performance monitoring, demand management, schedule optimization and social media analytics.


Industrial Operations Supporting Industry 4.0
ASAKURA Takayoshi

Industry 4.0 (The fourth industrial revolution) has the purpose of enabling mass-customization in the manufacturing industries and in significantly reducing manufacturing costs by advancing the entire SCM. In the near future this trend is expected to spread throughout the manufacturing plants of Japan. This paper focuses on the industrial operations (operations management) that are applying the AI technology and by which especially important reforms may be expected to be introduced. Industrial operations issues related to cost minimization and quality maximization that are important in applying AI technology are also introduced as are the AI technology procedures used by NEC in this field.


A world-leading array of AI technologies

Video Face Recognition System Enabling Real-time Surveillance
IMAOKA Hitoshi

The recent sharp increase in terrorist attacks has made the security of public facilities such as airports, restaurants and hotel lobbies a global issue. The face image in a passport is an only data to be used for personal authentication worldwide, regardless of nationality. In addition, the face image recognition offers a unique advantage compared to other personal authentication technologies because it enables to authenticate people from a distant location such as personal identification through surveillance cameras. NEC joined the Face Recognition Vendor Tests of the U.S. National Institute of Standards and Technology (NIST) in 2009 and gained the top ranking three times consecutively. This paper introduces the NEC video face recognition technology for use in video surveillance.


Optical Vibration Sensing Technology Improves Efficiency of Infrastructure Maintenance
TAKADA Jun

Optical vibration sensing technology is the world’s first technology to utilize video to estimate the level of deterioration within critical public infrastructure, such as bridges. This technology precisely measures surface vibrations on the structure being analyzed and then estimates the degree of internal deterioration - i.e., cracks, flaking, and cavities - based on the characteristics of those vibrations. This means that it is now possible to analyze the interior deterioration of a massive structure from ground level with no need for scaffolding. As well as enabling early detection of deterioration and significantly reducing inspection costs, optical vibration sensing makes it possible to quantify the amount of internal deterioration irrespective of how skilled or experienced the inspectors are. Future iterations of this technology will deliver even more sophisticated capabilities as we continue to improve deterioration transition forecasting and automatic generation of maintenance plans.


Automated Security Intelligence (ASI) with Auto Detection of Unknown Cyber-Attacks
TAGATO Hiroki, SAKAE Yoshiaki
KIDA Koji, ASAKURA Takayoshi

It has now become necessary to adopt countermeasures against cyber-attacks that are becoming more sophisticated as the years pass. Automated Security Intelligence (ASI) is a self-learning, system anomaly detection technology that collects detailed operations logs from PCs and servers using monitoring software. It then generates the usual status of the surveyed system by applying machine learning (AI) to the log and compares it with the current system operations in order to detect even unidentified attacks. When this technology is applied to a security monitoring system, more robust security can be implemented thanks to detection throughout the attack process, including in the intermediate stages such as “Exploration” and “Installation” inside the system, as well as at the initial and final stages of the attack stages.


“Profiling Across Spatio-temporal Data” Technology to Enable Detection of Suspicious Unregistered Individuals among Multiple Surveillance Camera Images
NISHIMURA Shoji, LIU Jianquan, ARAKI Takuya

The past few years have seen the introduction of huge numbers of surveillance cameras in urban locations, both private and public. To date, the images recorded by these cameras have largely been used in the investigation of crimes after the crimes have occurred. However, with the growing threat of crimes that pose a significant risk of major damage or loss of life such as terrorist attacks, governments and security institutions are seeking ways to detect and prevent such incidents before they occur. This paper discusses new image search technology that makes it possible to detect not only those individuals already registered in a security database such as conventional wanted suspects, but also individuals who may not be registered but exhibit suspicious behavior. Using NEC’s face recognition technology and analyzing pedestrian appearance patterns, this technology is expected to help prevent crimes by facilitating early detection of suspicious individuals.


Customer Profile Estimation Technology for Implementation of Precise Marketing
NAKADAI Shinji, OYAMADA Masafumi

The assignment of features to individual products (product DNA as seen from the perspective of the customer) is becoming popular. Although the aim of this technology is to implement marketing based on the hobbies and tastes of customers, it is often accompanied in practice by troublesome issues. NEC’s customer profile estimation features low labor input and yields high accuracy. It is a technology based on NEC’s unique strategy of relational data mining technology that predicts the profiles (job, hobby, annual income, etc.) of all customers, being evaluated from some of entire data. When the customer profiles are enhanced using this estimation technology, it becomes possible to discover the real needs related to buying, display, new product planning and sales promotion for “individuals” who have previously been overlooked. The technology will thereby enable the effective planning of more specific measures.


Quality Control in Manufacturing Plants Using a Factor Analysis Engine
ASAKURA Takayoshi, OCHIAI Katsuhiro

A factor analysis engine is an analysis technology that secures the product quality of manufacturing industry production plants. Traditionally, when the quality of a product deteriorates an expert analyzer is employed to assess the quality deterioration factors by using experience based data analysis. However, this process often takes a long time or is unable to accurately specify the factors. The factor analysis engine solves these issues by automatically analyzing the time-series data of the sensors installed in the production facilities, by identifying the factors that are causing the quality deterioration and by providing information on remedial action that leads to solutions. This paper introduces the characteristics and mechanisms of the factor analysis engine and also describes an example of its application.


From Prediction to Decision Making - Predictive Optimization Technology -
FUJIMAKI Ryohei, MURAOKA Yusuke
ITO Shinji, YABE Akihiro

Energy demand forecasting for each city area, sales prediction for each product in a retail store, prediction of decline in customer satisfaction toward provided services, etc. The importance of accumulation and utilization of big data is now recognized widely. Progress in the machine learning technologies, such as in NEC’s heterogeneous mixture learning technology is enabling highly precise data-driven predictions. This paper introduces decision making and actual application cases using artificial intelligence (AI) together with the associated challenges that go beyond prediction. The “Predictive Optimization” discussed here is the state-of-the-art technology that allows us to make decisions (what should we do) based on predictions (what will happen).


Dynamic Bus Operations Optimization with REFLEX
Konstantinos Gkiotsalitis, Nitin Maslekar

High frequency bus operations in metropolitan areas should provide a reliable service to passengers by reducing their EWTs at bus stations. In several metropolis such as London and Singapore bus operators receive monetary incentives if they manage to reduce the EWTs of passengers or penalties if they fail to do so. However, optimizing the regularity of bus operations by preventing bus bunching is a computationally intractable problem and bus operators are not able to schedule the daily bus trips in an optimal way. Therefore, they rely on in-house expertise to manage their operations without fully exploiting the potential of applying operational control measures such as dispatching and bus holding at stations. For this reason, our work models the regularity-based bus operations and introduces REFLEX, an AI agent which enables the implementation of bus control actions. REFLEX uses a heuristic Sequential Exterior Point Greedy method for optimizing bus service operations and is tested in a trial with a major bus operator in Asia. REFLEX was able to optimize bus services with 200+ daily trips in just 1-2 minutes of computational time, while providing 17-35% theoretical service regularity improvement subject to a set of strict operational constraints such as adherence to layover times and departure frequencies ranges. Thanks to REFLEX rapid computation, bus operators can also simulate further service regularity improvements resulting from relaxing some operational constraints or adding more trips. Looking further, REFLEX can be combined with e-paper based timetable displays to provide a fully dynamic operational schedule and will be extended to support multi-modal interchange optimization.


Individual Recognition Based on the Fingerprint of Things Expands the Applications of IoT
ISHIYAMA Rui, TAKAHASHI Toru, KUDO Yuta

Recently in the manufacturing industry, there is an increasing trend to collect and analyse the big data of the history of manufacturing and using objects so as to connect those results to production innovations and maintenance & inspection services. Individual recognition is fundamental for the acquisition of data of the individual objects. Nevertheless, the traditional methods of identification by attaching barcodes or RFID tags to objects are not capable of recognizing the individual electronic components that are reducing in size, or the precision machines or materials that do not accept surface processing. This paper introduces applications of the Fingerprint of Things in the individual recognition and IoT. These enable the identification of individual parts of even uniformly manufactured industrial products by image recognition of the finely depicted surface patterns (Fingerprint of Things) in the images obtained with a regular camera.


NEC’s open innovation is generating exciting developments in AI technology

Achieving a more omoroi society through the application of the brain’s yuragi (fluctuations) to bring computer energy consumption down to an amazingly ultralow level

In the not-too-distant future, the demands of ICT will require new computing technology with energy efficiency vastly superior to what we have today. To achieve this, scientists are taking a tip from the human brain, which consumes a fraction of the amount of power used by a computer. The secret of the brain’s efficiency is believed to lie in its yuragi (stochastic properties of biological systems). To find out what this could mean for the future of computing, Toshiyuki Kano, Senior Technical Chief at NEC Central Research Laboratories sat down with an expert in this field, Toshio Yanagida, Specially Appointed Professor of Osaka University.


What is Brain-Morphic AI?

Brain-Morphic AI is a revolutionary new concept in artificial intelligence (AI) that challenges the conventional approach to AI development. Instead of using digital technology, Brain-Morphic AI seeks to more closely approximate the actual human brain by using analog circuits. Advanced trials of this new technology have now been implemented under the direction of Kazuyuki Aihara, one of Japan’s leading scientists in the field of mathematical engineering. Here, Dr. Aihara chats with Yuichi Nakamura, the general manager of NEC’s System Platform Research Laboratories, about the various elements required for AI to reproduce the brain.


Combining AI with simulation technology facilitates decision-making even under conditions where data is limited

NEC and the National Institute of Advanced Industrial Science and Technology (AIST) have teamed up to establish the NEC-AIST AI Cooperative Research Laboratory in Artificial Intelligence, which conducts joint research and development relating to AI. Here, Akio Yamada, General Manager of NEC’s Data Science Research Laboratories talks to Takashi Washio, who heads the cooperative research laboratory about the applications of AI in social systems.


AI Technology Brand “NEC the WISE”


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