SoftBank Group's Vision Fund will lead a ¥13.7 billion ($101 million) Series D fundraising into Japanese startup LegalForce, in the world's biggest tech investor's fourth outlay in its home country. LegalForce, which uses AI to screen contracts for loopholes and potential legal risks, said other participants in the round include new investors Sequoia Capital China and Goldman Sachs Group, as well as existing backers Mitsubishi UFJ Capital and Mizuho Capital. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites. If this does not resolve the issue or you are unable to add the domains to your allowlist, please see this support page.
SoftBank Group Corp. is lining up startup investments in Japan, aggressively pursuing entrepreneurs in its home market for the first time since its Vision Fund's launch. The Japanese startup scene is going through a revival, helped by an influx of young talent from private equity funds and consulting firms, said Kentaro Matsui, a managing partner at the Vision Fund who overseas Japan investments. Combined with a shift in strategy to invest smaller sums than its previous threshold of $100 million (¥12.8 billion), this has meant more opportunities for the world's largest tech fund to invest at home, he said. Japan's weight in SoftBank's overall portfolio will "definitely" increase, Matsui said in an interview in Tokyo. "The caliber of people in the companies we are investing in is clearly different" compared with 2018 or 2019, he said.
SoftBank Group Corp.-backed Light is struggling to raise funds after the world's largest tech investor balked at putting more money into the startup, people familiar with the matter said. SoftBank owns about 30% of Light through its first Vision Fund, which led an injection of $121 million (¥15.4 billion) into the advanced camera developer in 2018. SoftBank's continued support is critical for the cash-strapped startup, which had been spending millions of dollars to expand into self-driving tech at SoftBank's urging. Hurt by plunging tech valuations, SoftBank is walking away from some of its loss-making portfolio firms to comply with stricter investment criteria, said the people, who asked not to be identified because the matter was not public. Many of the two Vision Funds' portfolio of 300-plus companies are loss-making.
Wednesday, the MLCommons, the industry consortium that oversees a popular test of machine learning performance, MLPerf, released its latest benchmark test report, showing new adherents including computer makers ASUS, H3C, and ZhejiangLab, a research institute formed by the Zhejiang province government in China, Zhejiang University and Chinese retail and AI giant Alibaba. Those parties join frequent submitters Nvidia, Qualcomm, Dell, and Microsoft. The MLCommons's executive director, David Kanter, lauded the record number of submissions, over 3,900. Those results span a wide range of computing, from data centers down to what is known as "TinyML," running on devices such as embedded microchips that sip fractions of a watt of power. "This is a huge dynamic range," said Kanter.
Samsung has applied approximately 60 new AI models run by the neural processing unit (NPU) to optimise the functions of the Galaxy S22 Ultra smartphone camera, a company executive said. This has allowed the South Korean tech giant to offer camera experiences that can satisfy casual users with the best photographs possible and professional users with RAW files equivalent to those taken on DSLR cameras, said Joshua Sungdae Cho, vice president and head of visual software R&D at Samsung's MX Business, in an interview with ZDNet. "We've applied NPUs to our smartphones for the first time three years ago," said Cho. "At the time, these NPU ran approximately 10 AI models. On the Galaxy S22 Ultra, there are now 60 AI models. Basically, the NPU is involved in nearly all functions of the cameras."
Artificial Intelligence has applications in all fields including telecommunications. Mobile data analytics, total network optimization, enhancing security, improvement of network operations, control of operational cost and network design are some of the areas where AI can be deployed. Prediction of consumer behaviour, introduction of such services which will be appreciated by him/her, launching of customised services, reduction of power consumption, improving resilience of the network, predictive maintenance, optimum utilisation of network elements, seamless provision of services and real time fraud detection and fraud prevention are other interesting applications of AI. As operators move from 4G to 5G implementation, network complexity and cost sharply increase because of implementation of Customer Experience Management (CEM), IOT, SDN (Software Defined Network), NFV (Network Function Virtualisation), 5G Radio access equipment and core infrastructure, 5G for private networks etc. But the average revenue per user is stagnating.
Telecom: Verdict lists the top five terms tweeted on telecommunications in Q4 2021 based on data from GlobalData's Technology Influencer Platform. The top trends are the most mentioned terms or concepts among Twitter discussions of more than 150 telecommunications experts tracked by GlobalData's Technology Influencer platform during the fourth quarter (Q4) of 2021. India's 5G network roll out plans, a partnership between wireless voice and data services provider DISH Wireless and technology company Cisco, and new 5G testing capabilities for non-terrestrial networks (NTNs) were among the popular discussions on 5G in Q4 2021. Madhav Seth, vice president of smartphone company realme, shared an article on India's Department of Telecommunication (DoT) confirming its plans to roll out 5G network in the country in 2022. The network will initially be launched in 13 cities where 5G trials were conducted by telecom operators Airtel, Jio and Vi to perform end-to-end testing of 5G and develop 5G products and services.
Samsung said on Friday it will commence a software update "as soon as possible" to address consumer complaints about a preinstalled app limiting the performance of Galaxy S22 smartphones. The issue stems from the Game Optimising Service (GOS) app on the phones, which automatically limits the performance of devices when it detects a gaming app is in operation. The South Korean tech giant said it plans to add an option in its game launcher app to allow users to prioritise performance through the software update. More details on how this option will work are expected to be announced later. Samsung previously explained that the GOS app was put on devices to prevent them from overheating and losing battery too quickly during gaming for consumer safety.
Mobile edge computing (MEC) is considered a novel paradigm for computation-intensive and delay-sensitive tasks in fifth generation (5G) networks and beyond. However, its uncertainty, referred to as dynamic and randomness, from the mobile device, wireless channel, and edge network sides, results in high-dimensional, nonconvex, nonlinear, and NP-hard optimization problems. Thanks to the evolved reinforcement learning (RL), upon iteratively interacting with the dynamic and random environment, its trained agent can intelligently obtain the optimal policy in MEC. Furthermore, its evolved versions, such as deep RL (DRL), can achieve higher convergence speed efficiency and learning accuracy based on the parametric approximation for the large-scale state-action space. This paper provides a comprehensive research review on RL-enabled MEC and offers insight for development in this area. More importantly, associated with free mobility, dynamic channels, and distributed services, the MEC challenges that can be solved by different kinds of RL algorithms are identified, followed by how they can be solved by RL solutions in diverse mobile applications. Finally, the open challenges are discussed to provide helpful guidance for future research in RL training and learning MEC.
Network virtualization (NV) is a technology with broad application prospects. Virtual network embedding (VNE) is the core orientation of VN, which aims to provide more flexible underlying physical resource allocation for user function requests. The classical VNE problem is usually solved by heuristic method, but this method often limits the flexibility of the algorithm and ignores the time limit. In addition, the partition autonomy of physical domain and the dynamic characteristics of virtual network request (VNR) also increase the difficulty of VNE. This paper proposed a new type of VNE algorithm, which applied reinforcement learning (RL) and graph neural network (GNN) theory to the algorithm, especially the combination of graph convolutional neural network (GCNN) and RL algorithm. Based on a self-defined fitness matrix and fitness value, we set up the objective function of the algorithm implementation, realized an efficient dynamic VNE algorithm, and effectively reduced the degree of resource fragmentation. Finally, we used comparison algorithms to evaluate the proposed method. Simulation experiments verified that the dynamic VNE algorithm based on RL and GCNN has good basic VNE characteristics. By changing the resource attributes of physical network and virtual network, it can be proved that the algorithm has good flexibility.