Common sense tells us that when something grows too fast, it's usually not a good thing. And that's exactly what the bubbly space of artificial intelligence looks like right now. In the past five years, the number of privately-owned AI companies that received venture capital funding have grown more than 500%, and the average funding size has almost tripled. And despite industry insiders' repeated warning of a forming "AI bubble," the frontrunners in this cash-pumping game have shown no signs of slowing down. SEE ALSO: What Microsoft's $1 Billion Investment in OpenAI Could Achieve Last month, Japanese investment powerhouse SoftBank Group, which turned Silicon Valley upside down in 2017 and 2018 with its $100 billion Vision Fund, announced that it was ready to launch a second Vision Fund and already had $108 billion secured from upstream investors.

It takes powerful technology to connect our brands and partners with an audience of 1 billion. Nearly half of Verizon Media employees are building the code and platforms that help us achieve that. Whether you're looking to write mobile app code, engineer the servers behind our massive ad tech stacks, or develop algorithms to help us process 4 trillion data points a day, what you do here will have a huge impact on our business--and the world. As Verizon's media unit, our brands like Yahoo, TechCrunch and HuffPost help people stay informed and entertained, communicate and transact, while creating new ways for advertisers and partners to connect. Millions of people visit the Yahoo homepage for news, sports, finance, email, and more.

It takes powerful technology to connect our brands and partners with an audience of 1 billion. Nearly half of Verizon Media employees are building the code and platforms that help us achieve that. Whether you're looking to write mobile app code, engineer the servers behind our massive ad tech stacks, or develop algorithms to help us process 4 trillion data points a day, what you do here will have a huge impact on our business--and the world. As Verizon's media unit, our brands like Yahoo, TechCrunch and HuffPost help people stay informed and entertained, communicate and transact, while creating new ways for advertisers and partners to connect. About Verizon Media Verizon Media is a values-led company committed to building brands people love.

SoftBank Group Corp. said Wednesday its group net profit in the April-June period jumped more than threefold to a record ¥1.12 trillion ($10.6 billion) from a year earlier -- marking the best quarter for a Japanese firm since 2004 -- boosted by a special profit from selling part of its stake in Chinese e-commerce giant Alibaba Group Holding Ltd. SoftBank Group said its operating profit fell 3.7 percent to ¥688.82 billion in the three months that ended June 30 on sales of ¥2.34 trillion, up 2.8 percent on a consolidated basis. The company logged the largest group net profit on a quarterly basis among 400 major firms listed on bourses operated by Japan Exchange Group Inc. since Nomura Holdings Inc. started compiling such data in 2004. The investment giant said it booked a one-time gain of ¥1.22 trillion in the quarterly period following the completion of the partial sale of the stake in Alibaba. The company's profit was also boosted by gains from investments in technology startups made by its Vision Fund, through which SoftBank made investments in 81 companies as of the end of June. "It is remarkable for us to mark a (group net) profit of more than ¥1 trillion in a quarter for the first time," said Chairman and CEO Masayoshi Son at a news conference in Tokyo.

--Channel charting (CC) has been proposed recently to enable logical positioning of user equipments (UEs) in the neighborhood of a multi-antenna base-station solely from channel-state information (CSI). CC relies on dimensionality reduction of high-dimensional CSI features in order to construct a channel chart that captures spatial and radio geometries so that UEs close in space are close in the channel chart. In this paper, we demonstrate that autoencoder (AE)-based CC can be augmented with side information that is obtained during the CSI acquisition process. More specifically, we propose to include pairwise representation constraints into AEs with the goal of improving the quality of the learned channel charts. We show that such representation-constrained AEs recover the global geometry of the learned channel charts, which enables CC to perform approximate positioning without global navigation satellite systems or supervised learning methods that rely on extensive and expensive measurement campaigns.

Learning-based Adaptive Bit Rate~(ABR) method, aiming to learn outstanding strategies without any presumptions, has become one of the research hotspots for adaptive streaming. However, it is still suffering from several issues, i.e., low sample efficiency and lack of awareness of the video quality information. In this paper, we propose Comyco, a video quality-aware ABR approach that enormously improves the learning-based methods by tackling the above issues. Comyco trains the policy via imitating expert trajectories given by the instant solver, which can not only avoid redundant exploration but also make better use of the collected samples. Meanwhile, Comyco attempts to pick the chunk with higher perceptual video qualities rather than video bitrates. To achieve this, we construct Comyco's neural network architecture, video datasets and QoE metrics with video quality features. Using trace-driven and real-world experiments, we demonstrate significant improvements of Comyco's sample efficiency in comparison to prior work, with 1700x improvements in terms of the number of samples required and 16x improvements on training time required. Moreover, results illustrate that Comyco outperforms previously proposed methods, with the improvements on average QoE of 7.5% - 16.79%. Especially, Comyco also surpasses state-of-the-art approach Pensieve by 7.37% on average video quality under the same rebuffering time.

In their work to unearth evidence of fraudulent activities, forensic accounting investigators dig through diverse data looking for anomalies that suggest something is just not right. But as the massive volumes of data collected by companies balloon, this task has become increasingly arduous, time-consuming and humanly impossible. Instead of investigators manually reviewing spreadsheet rows and columns, looking for three or four data elements that together indicate a suspicious transaction, ML can peruse thousands of data elements -- instantly. The regrettable consequence is the greater chance of a well-thought-out scam slipping through the cracks. A case in point is healthcare fraud, which has been estimated to cost the United States tens of billions of dollars annually.

The internet activity records (IARs) of a mobile cellular network posses significant information which can be exploited to identify the network's efficacy and the mobile users' behavior. In this work, we extract useful information from the IAR data and identify a healthy predictability of spatio-temporal pattern within the network traffic. The information extracted is helpful for network operators to plan effective network configuration and perform management and optimization of network's resources. We report experimentation on spatiotemporal analysis of IAR data of the Telecom Italia. Based on this, we present mobile traffic partitioning scheme. Experimental results of the proposed model is helpful in modelling and partitioning of network traffic patterns.

There is a growing interest in the wireless communications community to complement the traditional model-based design approaches with data-driven machine learning (ML)-based solutions. While conventional ML approaches rely on the assumption of having the data and processing heads in a central entity, this is not always feasible in wireless communications applications because of the inaccessibility of private data and large communication overhead required to transmit raw data to central ML processors. As a result, decentralized ML approaches that keep the data where it is generated are much more appealing. Owing to its privacy-preserving nature, federated learning is particularly relevant for many wireless applications, especially in the context of fifth generation (5G) networks. In this article, we provide an accessible introduction to the general idea of federated learning, discuss several possible applications in 5G networks, and describe key technical challenges and open problems for future research on federated learning in the context of wireless communications.

Tech's biggest players have fully embraced the AI revolution. Apple, Qualcomm and Huawei have made mobile chipsets that are designed to better tackle machine-learning tasks, each with a slightly different approach. Huawei launched its Kirin 970 at IFA this year, calling it the first chipset with a dedicated neural processing unit (NPU). Then, Apple unveiled the A11 Bionic chip, which powers the iPhone 8, 8 Plus and X. The A11 Bionic features a neural engine that the company says is "purpose-built for machine-learning," among other things.