Category: misc

Posted on LinkedIn. I got the following question from the editor of an internet news site: In about 100 words, what do you think will happen in the course of 2021 with respect to artificial intelligence(AI), machine learning(ML) or their applications in various industries? Here is my reply: This year more and more people will realize the gap between highly-hyped AI technologies and real business problems. Although AI, especially deep learning, has been advertised as a technology that had eliminated the need for tedious manual work in any problem, they will learn that the reality is not that simple, except for a few specific tasks such as image recognition and…

Shared my thoughts on Trusted AI in a LinkedIn article. When talking about Trusted AI, people implicitly assume that AI tends to be anti-human without a certain penalty provided by humans.  I have a different thought. Machine learning as the core technology of AI is merely a mathematical technique to capture certain patterns hidden inside data. It should be neutral in nature to gender, race, sexual orientation, or any attributes of the samples. If so, we can think of another research direction for Trusted AI: AI as a tool for democratizing infrastructures.  “Frugal” Innovations for Africa A project named “Frugal Innovations for Africa” is one of the projects I led…

Posted on LinkedIn article. I recently attended AAAI-21 to present my work titled “Anomaly Attribution with Likelihood Compensation.” The paper falls into the field of explainability of AI (XAI), which is one of the hottest research areas in AI. IBM Research has been among the main contributors in the area for several years. My organization, led by Saska Mojsilovic, an IBM Fellow, represents XAI research in IBM. Broadly speaking, the work is to explain the behavior of a black-box AI model. This sounds like a familiar topic in XAI, but it turned out that the problem we wanted solve was not able to be solved by existing XAI methods. The task is to explain deviations…

I happily announce that my new book titled “大規模計算時代の統計推論” has just come out. It’s available at Amazon.co.jp. The book is a Japanese translation of the celebrated book “Computer Age Statistical Inference” by maestro statisticians Prof. B. Efron and Prof. Hastie. I supervised the translation project and translated a few chapters myself. Although the topics covered are mostly basic, the description of those topics is deep and profound. What I was particularly interested in is the way statisticians look at machine learning. The authors say: In the absence of optimality criteria, either frequentist or Bayesian, the prediction community grades algorithmic excellence on performance within a catalog of often-visited examples such as…

A paper on decentralized collaborative learning has been accepted to IJCAI 19, one of the top AI conferences, for presentation at Macau, China, in August. The paper starts with giving a compliment to Bitcoin for its innovative idea for securing data consistency, which is usually called “Proof-of-Work”. Although the original paper describes the idea as voting by computation power, I think the key idea is not just a voting but its stochastic nature. My paper presents a simple but efficient way of doing “(relatively) secure” collaborative learning with a particular focus on IoT (internet-of-things) applications, where cryptographically rigorous security would be too much. Hope I can see many people sharing…

It’s been a while since I posted last time. Now that we are looking at the year-end holiday season, I’m giving here some updates for the year of 2018. Blockchain I was involved in a research project on Blockchain this year. I was curious what kind of people were working on it. Unfortunately, I didn’t get many collaboration opportunities, but I was able to finish two papers on a new framework of Blockchain (one has been accepted as a workshop paper, the other is under review). Probabilistic tensor regression model My paper on probabilistic tensor regression has been accepted to AAAI-19. A major application I described in the paper is…

My paper on an image-based traffic estimation method has just been published. Let me cite a part of my email sent to the project members and managers: It’s been more than five years since we started this project in Japan. When Katsuki-san, a new hire at TRL at that time, started looking at traffic images at accessKenya.com, no one imagined that this project would receive global attention in a year or so. It was way before IBM started the extensive campaign on Cognitive Computing. I’m glad to see that this project has become one of the major activities at Africa Lab. It was one of the most exciting projects in…

My paper on questionnaire data analysis has just come out. This paper is the first paper to propose an supervised extension of the Item Response Theory, which is an interesting probabilistic model developed in the area of psychometrics. Tsuyoshi Idé and Amit Dhurandhar, “Supervised item response models for informative prediction,” Knowledge and Information Systems, 51 (2017) 235-257. @Article{Ide2017KAIS, author=”Tsuyoshi Id{\’e} and Amit Dhurandhar”, title=”Supervised item response models for informative prediction”, journal=”Knowledge and Information Systems”, year=”2017″, volume=”51″, number=”1″, pages=”235–257″, }

I’m just done with renewal of my website. It had been on my to-do list for many years. I’m really glad that I finally got it done at the beginning of 2017. In this page, I’ll be posting updates on my research (and possibly other related) activities.