Industry


AI robot duo to guide visitors during test trial at Tokyo Station

The Japan Times

East Japan Railway Co. on Wednesday showed the media a test trial of two artificial intelligence robots designed to guide passengers at Tokyo Station. The robots -- Pepper of SoftBank Robotics Corp. and SEMMI of German railway company Deutsche Bahn AG -- were deployed at an information desk in a shopping and dining center called Gransta on the basement floor of the station. Visitors can ask for directions to stores and restaurants in the facility in languages including Japanese, English and Chinese, JR East said. The trial, which began Monday and will last through May 31, comes as part of a technological exchange between JR East and Deutsche Bahn that began in 1992. They will look at the machines' capabilities and visitors' reactions to their appearance.


Time for a change? Japan wants international media to put family names first

The Japan Times

Foreign Minister Taro Kono plans to ask overseas media outlets to write the names of Japanese people with the family name first, as is customary in the Japanese language. If realized, the new policy would mark a major shift in the country's long-running practice for handling Japanese names in foreign languages -- which began in the 19th to early 20th centuries amid the growing influence of Western culture. At a news conference Tuesday, Kono said that Prime Minister Shinzo Abe's name should be written as "Abe Shinzo," in line with other Asian leaders such as Chinese President Xi Jinping and South Korean President Moon Jae-in. Now is the right time to make the change, given that the Reiwa Era has just begun and several major events -- including next month's Group of 20 summit and the 2020 Tokyo Olympics -- are approaching, Kono said. "I plan to ask international media organizations to do this. Domestic media outlets that have English services should consider it, too," he said, citing a report released in 2000 by the education ministry's National Language Council that said it was desirable to write Japanese names with the family name first in all instances.


Ford's self-driving cars may have delivery robots because humans are too lazy

FOX News

Fox News Flash top headlines for May 22 are here. Check out what's clicking on Foxnews.com Ford is developing self-driving delivery vehicles it plans to launch in 2021, but there's a problem. If there isn't a driver, who's going to bring the package or pizza to your door? In tests with faux-autonomous Domino's Pizza cars, Ford discovered that a lot of people were simply too lazy to make the trip to the curb to get their orders from the car themselves, so it came up with the obvious solution: robots.


Testing the ability of unmanned aerial systems and machine learning to map weeds at subfield scales: a test with the weed Alopecurus myosuroides (Huds) - Lambert - - Pest Management Science - Wiley Online Library

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The core objective of plant population ecology is to understand changes in numbers of individuals/organisms across time and space.1 Achieving this depends on methods that permit plants to be mapped and monitored at informative scales.2-4 Surveys of plant populations have been undertaken using a variety of different methods such as transect sampling, quadrat sampling and with unmanned aerial systems (UAS).5-7 Each of these methods has an inherent trade‐off between the area that can be surveyed and the intensity at which the subjects in that area can be studied.8 Transect and quadrat sampling can be used for either small area, high‐intensity studies or large area, low‐intensity studies, but typically not both.9 UAS present a unique opportunity for ecological monitoring because, potentially, they can yield data across both large spatial areas and at high survey intensity.


Why Google believes machine learning is its future

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One of the most interesting demos at this week's Google I/O keynote featured a new version of Google's voice assistant that's due out later this year. A Google employee asked the Google Assistant to bring up her photos and then show her photos with animals. She tapped one and said, "Send it to Justin." The photo was dropped into the messaging app. From there, things got more impressive.


r/MachineLearning - [R] Machine Learning Reproducibility Challenges and DVC

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When ML models need to be regularly updated in production, a host of challenges emerges. No one tool can do it all for you - organizations using a mix of Git, Makefiles, ad hoc scripts and reference files for reproducibility.


AI Training Software Might Be The Future Of eSports [Infographic]

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The competitive video gaming industry has become a serious business over the past few years. As prize pools and viewership continue to grow year after year the industry is attracting more and more players. With new players coming in every day, the competition is getting saturated with talented professionals. How does one gain an edge in such a competitive world? Well, some would say to just practice as much as possible but I think we are getting to the point where that will not be enough.


A Gentle Introduction to Object Recognition With Deep Learning

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The model is significantly faster to train and to make predictions, yet still requires a set of candidate regions to be proposed along with each input image. Python and C (Caffe) source code for Fast R-CNN as described in the paper was made available in a GitHub repository. The model architecture was further improved for both speed of training and detection by Shaoqing Ren, et al. at Microsoft Research in the 2016 paper titled "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks." The architecture was the basis for the first-place results achieved on both the ILSVRC-2015 and MS COCO-2015 object recognition and detection competition tasks. The architecture was designed to both propose and refine region proposals as part of the training process, referred to as a Region Proposal Network, or RPN. These regions are then used in concert with a Fast R-CNN model in a single model design. These improvements both reduce the number of region proposals and accelerate the test-time operation of the model to near real-time with then state-of-the-art performance.


Data Analytics Performance Gap Ruins CX in Banking

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The mission of building one-to-one communication and engagement is not a new concept. Back in 1993, Don Peppers and Martha Rogers, Ph.D., proposed that organizations could use technology to gather information about, and to communicate directly with, individuals to form a personal bond. The book, The One to One Future: Building Relationships One Customer at a Time, stated that technology had made it possible and affordable to track individual consumers, to understand each person's individual journey, and to provide contextual offers at the optimal time of need. Six years later, internationally recognized best-selling author Seth Godin published Permission Marketing. He built a logical case for creating incentives for consumers to accept advertising voluntarily.


Create deep learning models with Flowpoints

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For those who like their dessert first: here's the finished model, and here's the colab for this example. A rather empty user-interface should show up on your screen. In the sidebar, click the Library-dropdown, and select TensorFlow. Now the code for our model will use TensorFlow instead of PyTorch. Next, click on the Theme-dropdown and select "orange".