Country Garden, a property developer in China, revealed that its subsidiary Qianxi Robot Catering Group (Qianxi Group) opened a restaurant complex operated completely by robots. Located in Shunde, which is a city in China's Guangdong province, the restaurant eliminates most human-to-human contact and may be a harbinger of how businesses plan to handle the aftermath of the coronavirus outbreak. "Country Garden assistant executive officer and Qianxi Group general manager Qiu Mi explained that Qianxi Group has built a complete industry chain encompassing back-end supply production (the centralized kitchens) and robotic cooking alongside the operation of restaurants and the management of robots," Country Garden shared. The restaurant complex is 2,000 square meters or about 21,527 square feet, and it has 20 robots equipped to serve a variety of dishes, including Chinese food, fast food, clay-pot rice and hot pot. The menu has 200 items, but they are available within 20 seconds of ordering.
Despite those obstacles, Indiana University School of Medicine faculty and Regenstrief Institute research scientists had their research published in Nature Communications on April 14, which is an even more significant feat considering one of the leading authors has been quarantined in Wuhan, China for the last two months of their work. The team consists of Affiliated Scientist Jie Zhang, PhD, Regenstrief Institute Research Scientist Kun Huang, PhD, both Indiana University School of Medicine faculty members, Jun Cheng, PhD, of Shenzhen University and colleagues including Liang Cheng, M.D. of IU School of Medicine. The study was led by Dr. Zhang, an assistant professor of medical and molecular genetics at IU School of Medicine. The work focuses on the application of machine learning and image analysis to help researchers distinguish a rare subtype of kidney cancer (translocational renal cell carcinoma, or tRCC) from other subtypes by examining the features of cells and tissues on a microscopic level. Dr. Zhang said the structural similarities have caused a high rate of misdiagnosis.
In autonomous driving, stereo vision-based depth estimation technology can help to accurately estimate the distance of obstacles, which is crucial for correct path planning of the vehicle. The stereo depth estimation problem has been formulated into a deep learning model with convolutional neural networks. However, these models need a lot of post-processing and do not have strong adaptive capabilities to ill-posed regions or new scenes. In addition, due to the difficulty of labeling the true ground depth for real circumstances, training data for the system is limited. A research team led by Dr. Zhang Qieshi from the Shenzhen Institutes of Advanced Technology (SIAT) of the Chinese Academy of Sciences has proposed a new technical solution to address the current depth estimation for autonomous driving.
CloudWalk has set up a facility in an AI industry park in Zhangjiang, displaying latest technologies and services including smart city projects. Chinese artificial intelligence firm CloudWalk Technology has raised 1.8 billion yuan (US$257 million) in its latest round of financing, the company said on Thursday. Investors in the new round include China Internet Industry Fund, Shanghai-based Guosheng Group, Guangzhou-based Nansha Financial Holdings and Industrial and Commercial Bank of China (ICBC), the country's biggest bank. The latest investment points to rebounded market confidence and marks CloudWalk's next step toward an initial public offering, the company said in a statement. CloudWork is among China's "Four AI Dragons," along with Megvii, SenseTime and Yitu, each of which is valued at more than US$1 billion.
Researchers from the Shenzhen Institutes of Advanced Technology (SIAT) of the Chinese Academy of Sciences have proposed a product image recognition method with guidance learning and noisy supervision. The study was published in Computer Vision and Image Understanding. Instead of collecting product images by laborious and time-intensive image capturing, the team introduced a novel large-scale dataset called Product-90. Consisting of more than 140K images with 90 categories, the dataset was related to Clothing1M (a large-scale public dataset designed for learning from noisy data with human supervision), but contained many more categories. Images were collected from reviews on e-commerce websites.
Anchor Neural World blockchain platform, is receiving increasing support from Japan's investment holdings and high-tech companies around the world. The recent strategic partnership agreement between Anchor Value (that invested in ANW blockchain platform) and the Shenzhen Drone Manufacturers Association (Shenzhen UAV Industry Association) has attracted attention not only from specialists but also from the major Japanese media. Publications in SANSPO, ASAHI, NICOVIDEO and DREAM NEWS highlighted not only the potential of artificial intelligence in the management and opportunities provided by blockchain technologies but also the uniqueness of ANW artificial intelligence engine, which can be used with maximum efficiency in any of spheres from finance to education. The visibility and recognisability of the ANW platform is already increasing according to the principle of a snow bowl: as companies join the project, its development becomes more powerful, which increases the attractiveness of the platform for innovative business.
In his book "Life 3.0", MIT professor Max Tegmark says "we are all the guardians of the future of life now as we shape the age of AI." Artificial Intelligence remains a Pandora's Box of possibilities, with the potential to enhance the safety, efficiency, and sustainability of cities, or destroy the potential for humans to work, interact, and live a private life. The question of how Artificial Intelligence will impact the cities of the future has also captured the imagination of architects and designers, and formed a central question to the 2019 Shenzhen Biennale, the world's most visited architecture event. As part of the "Eyes of the City" section of the Biennial, curated by Carlo Ratti, designers were asked to put forth their visions and concerns of how artificial intelligence will impact the future of architecture. Below, we have selected six visions, where designers reflect in their own words on aspects from ecology and the environment to social isolation. For further reading on AI and the Shenzhen Biennial, see our interview with Carlo Ratti and Winy Maas on the subject, and visit our dedicated landing page of content here.
If a high temperature or the absence of a mask is detected, the robots send an alert to the relevant authorities. All data can be transmitted to a centralised control center for real-time situational response and decision making. Moreover, although these robots are self-driving machines, they can also be controlled remotely, thereby saving manpower by reducing patrolling responsibilities and preventing cross-infection. These next-generation 5G patrol robots have already been spotted at airports and shopping malls in the cities of Guangzhou, Shanghai, Xi'an and Guiyang.
Accurate and timely metro passenger flow forecasting is critical for the successful deployment of intelligent transportation systems. However, it is quite challenging to propose an efficient and robust forecasting approach due to the inherent randomness and variations of metro passenger flow. In this study, we present a novel adaptive ensemble (AdaEnsemble) learning approach to accurately forecast the volume of metro passenger flows, and it combines the complementary advantages of variational mode decomposition (VMD), seasonal autoregressive integrated moving averaging (SARIMA), multilayer perceptron network (MLP) and long short-term memory (LSTM) network. The AdaEnsemble learning approach consists of three important stages. The first stage applies VMD to decompose the metro passenger flows data into periodic component, deterministic component and volatility component. Then we employ SARIMA model to forecast the periodic component, LSTM network to learn and forecast deterministic component and MLP network to forecast volatility component. In the last stage, the diverse forecasted components are reconstructed by another MLP network. The empirical results show that our proposed AdaEnsemble learning approach not only has the best forecasting performance compared with the state-of-the-art models but also appears to be the most promising and robust based on the historical passenger flow data in Shenzhen subway system and several standard evaluation measures.
Fiat Chrysler, in partnership with Chinese-based AutoX, wants to launch robo-taxis in China later this year, according to an announcement. The automobile maker, one of the "Big Three" in Detroit, has been casting a wide net for autonomous vehicle tech, and wants to keep pace with rivals in Detroit. Hong Kong's AutoX said it plans to integrate self-driving vehicle tech into a fleet of Chrysler Pacifica minivans -- the same vehicle that Waymo, Alphabet's subsidiary known for its work on autonomous vehicles, has often chosen to use. The two companies said they plan to offer up the self-driving cars this year in numerous Chinese cities, including Shenzhen and Shanghai, later in 2020. AutoX -- a lesser-known startup than others in the field -- was founded in 2016 by Jianxiong Xiao, a former Princeton professor who specializes in 3D learning, computer vision and robotics.