The impact of AI—can a robot get into the University of Tokyo?

Arai, Noriko

doi:10.1093/nsr/nwv011

Cited by 9 publications

(2 citation statements)

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“…Perhaps it is worth thinking about how to live with machines that do not read and understand, just pretend. Nevertheless, Todai Robot passed the entrance exam for the University of Tokyo [19]. Noriko Arai, an Artificial Intelligence researcher at the TED2017 conference [20], talked about how we can help children be extraordinary in the things in which we humans will always be better than Artificial Intelligence.…”

Section: The Machine and The Humanmentioning

confidence: 99%

The Evaluation of Transport Vehicle Suppliers

Velencei¹

2020

Proceedings of the 1st International Conference on Emerging Trends and Challenges in the Management Theory and Practice (ETCMTP

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If in a twenty-five million city somebody changes three undergrounds, it is transportation. If you go to the pub by bicycle in a village of one hundred and fifty people, it is also transportation, but it is different. In a city of hundreds of thousands of people, transportation is already a common concern, and so it is left to professionals to figure it out. This requires decisions. The purpose of this study is to illustrate the preparation of a decision. During the preparation of the decision, the experts of a Hungarian transport company of the city of one hundred thousand people had to consider that the application of the ROIs they considered important was not applicable due to the public service character and the long-life cycle. Expectations were very diverse, and most were nonquantifiable. The leader of the company commissioned a team of experts to prepare the decision, consisting of an economist, a railway mechanical engineer, a transport system organizer engineer, an electrical engineer and an electric plant manager, as well as a city politician and civil servant. As a consultant, I strived for a transparent decision-making process, using the algorithm of a knowledge-based system based on Artificial Intelligence. This knowledge-based system can overcome the limitations of a person's working memory and allow the exploration of the relationships between dozens of experts' expectations.

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Section: The Machine and The Humanmentioning

confidence: 99%

The Evaluation of Transport Vehicle Suppliers

Velencei¹

2020

Proceedings of the 1st International Conference on Emerging Trends and Challenges in the Management Theory and Practice (ETCMTP

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“…The Todai Robot project (Arai, 2015) is a challenge to solve Japanese university examinations, focusing towards attaining a high score in the National Center Test for University Admissions, and passing the entrance exam of the University of Tokyo (Todai). Although the Todai Robot project tries to achieve higher scores, their aim is rather to reveal the current performance and limitation of the existing AI technologies, using the examinations as its benchmark, similar to the COLIEE's legal bar exam task.…”

Section: Introductionmentioning

confidence: 99%

Analyzable Legal Yes/No Question Answering System using Linguistic Structures

Kano¹,

Hoshino²,

Taniguchi³

EPiC Series in Computing

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A central issue of yes/no question answering is usage of knowledge source given a question. While yes/no question answering has been studied for a long time, legal yes/no question answering largely differs from other domains. The most distinguishing characteristic is that legal issues require precise linguistic analysis such as predicates, case-roles, conditions, etc. We have developed a yes/no question answer-ing system for answering questions in a legal domain. Our system uses linguistic analysis, in order to find correspondences of predicates and arguments given problem sentences and knowledge source sentences. We applied our system to the COLIEE (Competition on Legal Information Extraction/Entailment) 2017 task. Our team shared the second place in this COLIEE 2017 Phase Two task, which asks to answer yes or no given a problem sentence. This result shows that precise linguistic anal-yses are effective even without the big data approach with machine learning, rather better in its analyzable design for future improvements.

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