Innovative technologies including 5G, artificial intelligence, virtual reality, and the Internet of Things (IoT) featured prominently today as the doors opened for ITU Telecom World 2017, an international platform for accelerating information and communication technology (ICT) innovations and partnerships. The event runs 25-28 September and aims to fast-track economic development and social good through its forum for sharing knowledge, exhibition for digital solutions, and business networking hub connecting nations, companies, organizations and individuals. It is organized annually by the International Telecommunication Union (ITU), the United Nations' specialized agency for ICT-related issues. Participants include exhibitors bringing with them the smartest ideas and entrepreneurial spirit of digital start-ups and ICT small- and medium-sized enterprises (SMEs) as well as governments, regulators, industry leaders, consultants and experts from emerging and developed markets around the world. The event was launched with a high-level opening ceremony featuring a live orchestra and traditional dance performance from Busan National Gukak Centre, and a video message delivered by Moon Jae-in, President of the Republic of Korea.

It was great catching up with Ericsson last week in San Francisco at the inaugural Mobile World Congress Americas conference. Ericsson is doing incredible work to advance innovation by partnering with operators globally around IoT and 5G deployments, ranging from testing new radio technology, like advanced MIMO, to new core 5G systems for providing network slicing, to applications like Autonomous vehicles. Ericsson's radio access network was also featured at Sprint's booth where the first 2.5 GHz Massive MIMO field tests were conducted using Sprint's spectrum and Ericsson's radios reaching peak speeds of more than 300 Mbps using a single 20 MHz channel! A great new use case for 5G was intelligent video streaming with Verizon for security and smart city applications, with streams coming to a central, video optimized repository in the core of the 5G network. This 5G overlay to an existing 4G network will provide benefits across multiple applications at the edge of the network from video cameras to drones to industrial control endpoints.

It's been a week since the Apple Watch Series 3 was revealed, and reviews of the wearable device have poured in -- pointing out the good and bad. The Apple Watch Series 3's major boost from previous models is its support for cellular LTE. However, the Wall Street Journal's Joanna Stern said there were issues with connectivity during the week in which she tested out the new models. Stern also said there were problems with Series 3's battery life. "You're lucky if the battery allows you to roam on cellular for longer than half a day--especially if you're making calls. And only a limited number of third-party apps work without the phone close by. Most worryingly, my colleague Geoffrey Fowler and I experienced cellular connectivity issues on three separate pre-production models, in two different states, on two different 4G LTE carriers. On the AT&T -connected models, the cellular connection dropped, calls were often choppy and Siri sometimes failed to connect. On the one that ran on T-Mobile, I experienced several dropped connections."

Now, Samsung will let you disable the dedicated Bixby key, making it much harder to summon the helper by accident, as Sammobile noticed. As for what purpose that key can serve after you disable Bixby, the answer, for now, seems to be... nothing. Last we checked, Samsung had "no plans" to allow you to remap it, but maybe now it's changed its mind. I'm not alone in my dislike of Bixby, which is impossible to completely deactivate and tries to impose itself on your life at inopportune moments, like when you're trying to, say, take a picture. My colleague Devindra, in testing the app for our "Life with AI" series, said that even after extensive training "it would still have the occasional brain fart that made me want to throw my phone out a window."

We envision a world where devices, machines, automobiles, and things are much more intelligent, simplifying and enriching our daily lives. They will be able to perceive, reason, and take intuitive actions based on awareness of the situation, improving just about any experience and solving problems that to this point we've either left to the user, or to more conventional algorithms. Artificial intelligence (AI) is the technology driving this revolution. You may have heard this vision or may think that AI is really about big data and the cloud, and yet Qualcomm's solutions already have the power, thermal, and processing efficiency to run powerful AI algorithms on the actual device -- which brings several advantages. AI is a pervasive trend that is rapidly accelerating thanks to vast amounts of data and progress in both algorithms and the processing capacity of modern devices.

Even though many people know Huawei only for their devices, Huawei is one of the biggest telecom infrastructure provider in the world and works with nearly all major telecom service providers across the globe. Huawei Connect is Huawei's annual Enterprise Business Group event where it covers many keynotes, panel discussions and expo regarding the latest technology in the field of telecommunications, wireless and cloud technology, ICT and more. It is one of the biggest ICT industry event and Huawei has just concluded the Huawei Connect 2017 event, 5th-7th September, in Shanghai at the Shanghai New International Expo Center Here is a brief summary of the event covering its highlights. Huawei Connect 2017 - Highlights and Summary Huawei Connect 2017 event started with a gala dinner for media on the night of 4th September. The first day of Huawei Connect 2017 started with the keynote speech on the topic Grow with the Cloud: Enabling an Intelligent World by Mr. Guo Ping, Rotating CEO of Huawei.

Artificial intelligence is becoming a defining characteristic in the smartphone market, powering personalization, virtual assistants, and even battery life. But AI takes a lot of computing power. To make up for that, companies like Apple and Huawei are adding additional chips into smartphones to handle such tasks. These are complementary to the existing CPU and GPU chips already in phones, and configured to be faster for one specific purpose--AI--at the expense of being able to do anything else. They also keep AI tasks from draining phone batteries as fast. Apple has dubbed theirs the Neural Engine, located inside the A11 Bionic chip, while Huawei's is called the Kirin 970.

Statistical methods for network data have received a lot of attention because of the wideranging applications of network analysis. There is now a large body of work on methods and models for networks, including the stochastic block model (SBM) [Holland et al., 1983], the degree-corrected stochastic block model (DCSBM) [Karrer and Newman, 2011], and the latent space model [Hoff et al., 2002], to name a few. While this gives the practitioner plenty of choices, there is a lot less work on the crucial question of how to select the best model for the data, as well as how to choose tuning parameters for the selected model, which is often necessary in order to fit it. In some specific problems, progress has been made recently, for instance, in the much-studied problem of community detection. Community detection is the problem of clustering network nodes into groups, and most of the methods proposed over the last twenty years or so require the number of communities K as input.

In this paper, we propose and study opportunistic bandits - a new variant of bandits where the regret of pulling a suboptimal arm varies under different environmental conditions, such as network load or produce price. When the load/price is low, so is the cost/regret of pulling a suboptimal arm (e.g., trying a suboptimal network configuration). Therefore, intuitively, we could explore more when the load is low and exploit more when the load is high. Inspired by this intuition, we propose an Adaptive Upper-Confidence-Bound (AdaUCB) algorithm to adaptively balance the exploration-exploitation tradeoff for opportunistic bandits. We prove that AdaUCB achieves $O(\log T)$ regret with a smaller coefficient than the traditional UCB algorithm. Furthermore, AdaUCB achieves $O(1)$ regret when the exploration cost is zero if the load level is below a certain threshold. Last, based on both synthetic data and real-world traces, experimental results show that AdaUCB significantly outperforms other bandit algorithms, such as UCB and TS (Thompson Sampling), under large load fluctuations.

Most distributed machine learning systems nowadays, including TensorFlow and CNTK, are built in a centralized fashion. One bottleneck of centralized algorithms lies on high communication cost on the central node. Motivated by this, we ask, can decentralized algorithms be faster than its centralized counterpart? Although decentralized PSGD (D-PSGD) algorithms have been studied by the control community, existing analysis and theory do not show any advantage over centralized PSGD (C-PSGD) algorithms, simply assuming the application scenario where only the decentralized network is available. In this paper, we study a D-PSGD algorithm and provide the first theoretical analysis that indicates a regime in which decentralized algorithms might outperform centralized algorithms for distributed stochastic gradient descent. This is because D-PSGD has comparable total computational complexities to C-PSGD but requires much less communication cost on the busiest node. We further conduct an empirical study to validate our theoretical analysis across multiple frameworks (CNTK and Torch), different network configurations, and computation platforms up to 112 GPUs. On network configurations with low bandwidth or high latency, D-PSGD can be up to one order of magnitude faster than its well-optimized centralized counterparts.