Osaka Metro unveils ticket gate with facial recognition tech

The Japan Times

OSAKA – Osaka Metro Co. showed a next-generation automated ticket gate with a facial recognition system to the media Monday. Aiming to introduce such gates at all of its train stations in fiscal 2024, ahead of the 2025 World Expo in the city of Osaka, the subway operator will start testing the gates Tuesday with some 1,200 employees. Through the test, the Osaka-based company hopes to identify problems and make improvements. This will be the first such test by a Japanese railway operator, according to Osaka Metro. The test, which is set to run through September 2020, will be conducted at four stations: Dome-mae Chiyozaki, Morinomiya, Dobutsuen-mae and Daikokucho.




New Books and Resources for DSC Members

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We are in the process of writing and adding new material (compact eBooks) exclusively available to our members, and written in simple English, by world leading experts in AI, data science, and machine learning. We invite you to sign up here to not miss these free books. This book is intended for busy professionals working with data of any kind: engineers, BI analysts, statisticians, operations research, AI and machine learning professionals, economists, data scientists, biologists, and quants, ranging from beginners to executives. In about 300 pages and 28 chapters it covers many new topics, offering a fresh perspective on the subject, including rules of thumb and recipes that are easy to automate or integrate in black-box systems, as well as new model-free, data-driven foundations to statistical science and predictive analytics. The approach focuses on robust techniques; it is bottom-up (from applications to theory), in contrast to the traditional top-down approach.


A Tutorial on Fairness in Machine Learning

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This post will be the first post on the series. The content is based on: the tutorial on fairness given by Solon Bacrocas and Moritz Hardt at NIPS2017, day1 and day4 from CS 294: Fairness in Machine Learning taught by Moritz Hardt at UC Berkeley and my own understanding of fairness literatures. I highly encourage interested readers to check out the linked NIPS tutorial and the course website. Fairness is becoming one of the most popular topics in machine learning in recent years. Publications explode in this field (see Fig1). The research community has invested a large amount of effort in this field.


Award CSI2025'19

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The 2019 IT Innovation & Excellence Awards, brought to you by CSI Mumbai Chapter, the fourth year of its presence. The awards ceremony will be held in Mumbai. The awards will recognize the very best in the area of Cognitive Technology and allied IT Industry including IoT, RPA, Innovative use of bots, Robotics, Innovative applications of cognitive application combine vision technology (including AR / VR), Advanced AI Application, Analytics and Machine Learning, Block chain. The leading organizations & individuals will be honoured and awarded for their innovation and excellence in this sector.


Artificial Intelligence Tutorial - It's your time to innovate the future - DataFlair

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Have you ever thought what would our lives be like in a world without Artificial Intelligence? Recall how you spend an average day of your life- you get up, then you check your smartphone. You reach your workplace, and then start working over the internet. Remember, most of your work takes place over cloud computing and other services the internet provides. Now picture that you have to look for an answer to something. For how long and in how many books are you going to keep searching for the answer? Let's take another example, you come back home and decide to order food online. Who really places the order if you are behind the screen?


Watching This AI-Powered LEGO Brick Sorter Is Extremely Satisfying

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The next time you clean up your LEGO collection, you're going to wish you had this machine at home to do the dirty work. Built pretty much entirely with LEGO pieces (plus a Raspberry Pi and some motors), this thing is able to sort virtually any LEGO piece that comes down its conveyer belts, thanks to artificial intelligence. It uses a neural network--or a set of algorithms that recognize patterns, similar to the human brain--to match the real-world LEGO pieces with 3D images of the pieces that the machine has been fed during training. The machine, which was built by YouTuber Daniel West, isn't the first of its kind--though it looks to be the most effective. There are plenty of other contraptions on YouTube showing crazy machines that others have built to sort LEGO bricks, like machines that sort LEGO axles, specifically, and others that spin plastic cups around to catch parts.


Researchers report breakthrough in 'distributed deep learning'

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Online shoppers typically string together a few words to search for the product they want, but in a world with millions of products and shoppers, the task of matching those unspecific words to the right product is one of the biggest challenges in information retrieval. Using a divide-and-conquer approach that leverages the power of compressed sensing, computer scientists from Rice University and Amazon have shown they can slash the amount of time and computational resources it takes to train computers for product search and similar "extreme classification problems" like speech translation and answering general questions. The research will be presented this week at the 2019 Conference on Neural Information Processing Systems (NeurIPS 2019) in Vancouver. The results include tests performed in 2018 when lead researcher Anshumali Shrivastava and lead author Tharun Medini, both of Rice, were visiting Amazon Search in Palo Alto, California. In tests on an Amazon search dataset that included some 70 million queries and more than 49 million products, Shrivastava, Medini and colleagues showed their approach of using "merged-average classifiers via hashing," (MACH) required a fraction of the training resources of some state-of-the-art commercial systems.


Statistics for Data Science in One Picture

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There's no doubt about it, probability and statistics is an enormous field, encompassing topics from the familiar (like the average) to the complex (regression analysis, correlation coefficients and hypothesis testing to name but a few). If you want to be a great data scientist, you have to know some basic statistics. The following picture shows which statistics topics you must know if you're going to excel in data science. The 5 Basic Statistics Concepts Data Scientists Need to Know. Basic Statistics Concepts Every Data Scientist Should know.