The Shenzhen-based company already makes the Kirin series of smartphone chips used in its high-end phones, and the Ascend series of chipsets for artificial intelligence computing launched in October. It said its latest 7 nanometre, 64-core central processing unit (CPU) would provide much higher computing performance for data centers and slash power consumption. It is based on the architecture of British chip design firm ARM - owned by Japan's SoftBank Group Corp - which is seeking to challenge the dominance in server CPUs of U.S. maker Intel Corp. Huawei aims to "drive the development of the ARM ecosystem", said Chief Marketing Officer William Xu. He said the chip has "unique advantages in performance and power consumption". Xu also said Huawei will continue its "long-term strategic partnership" with Intel.

The clash reflects American anxiety about China's rise as a potential competitor in telecommunications and other technology. Trump wants Beijing to roll back initiatives like "Made in China 2025," which calls for the state-led creation of global competitors in such fields as robotics and artificial intelligence. American officials worry those might erode U.S. industrial leadership.

Deep learning solutions are being increasingly used in mobile applications. Although there are many open-source software tools for the development of deep learning solutions, there are no guidelines in one place in a unified manner for using these tools towards real-time deployment of these solutions on smartphones. From the variety of available deep learning tools, the most suited ones are used in this paper to enable real-time deployment of deep learning inference networks on smartphones. A uniform flow of implementation is devised for both Android and iOS smartphones. The advantage of using multi-threading to achieve or improve real-time throughputs is also showcased. A benchmarking framework consisting of accuracy, CPU/GPU consumption and real-time throughput is considered for validation purposes. The developed deployment approach allows deep learning models to be turned into real-time smartphone apps with ease based on publicly available deep learning and smartphone software tools. This approach is applied to six popular or representative convolutional neural network models and the validation results based on the benchmarking metrics are reported.

Richard Yu, CEO of Huawei, recently gave an interview with CNBC. During the chat, Yu confirmed a Huawei voice assistant will launch globally at some point soon. This upcoming voice assistant would be a direct competitor to Google Assistant, Amazon's Alexa, and Apple's Siri, all of which already have a global presence. "In the beginning, we are mainly using Google Assistant and Amazon Alexa" for its smartphones and other smart products, Yu said. "We need more time to build our AI services…later, we will expand this outside of China."

Tech shapes our daily life, impacting not just how we read and work and play but how we interact, how we learn, how we grow. And just days from now in Las Vegas, CES (formerly the Consumer Electronics Show) will give us a window into what that will look like. With more than 4,400 exhibiting companies and more than 2.7 million square feet of exhibition hall, it's hard to glimpse tomorrow; that window is plastered in banner ads for Alexa, new televisions, and more iPhone cases than you can shake a fanboy at. Let me do the work for you. I've read the 687 emails I received in the last three weeks and synthesized all the announcements from LG, Samsung, Sony, Qualcomm, and everyone else.

CES 2019 will be my 16th consecutive jaunt to Las Vegas to see the latest and greatest that the consumer electronics industry has to offer. So I'm extremely confident in predicting that we'll see plenty of the following: Those, of course, are the table stakes -- the same trends that have been on display for the past three, five or even 10 years of the world's biggest electronics show. To that end, these are the biggest questions we have going into the show -- the answers to which will set the tone for the rest of 2019. Qualcomm showed off a 5G phone prototype in Hawaii last month. There is little doubt that 5G -- the next-generation wireless standard that promises hyperfast speeds with almost no latency -- is the key game-changing technology for 2019.

Over the years, we have been able to gain insights and learnings about how best to use our platform for a variety of real-world problems across finance, healthcare, telecommunications and more. Although our platform's SDK offers a full breadth of functionality to developers ranging from different machine learning capabilities to different options for generating topological models, we have been able to identify common patterns for data science problems that can be packaged in a manner that targets specific types of analyses.

Today, by aggregating and analyzing my search history, purchasing habits, YouTube views, and other data, AI can do a better job to predict which items I'm likely to buy in the future, and make it easy for me to make them mine. From the consumer's standpoint, the benefits of such AI--while they may at first seem a bit intrusive--are obvious. From the marketing side of things, AI is becoming even more valuable in a fiercely competitive, omnichannel world. By automating simple, repetitive tasks, AI tools can help marketers devote more of their attention to solving tough challenges and thinking more creatively. Searching for desired products on a brand's website can be more accurate and smarter than ever before with the addition of AI.

A typical way in which network data is recorded is to measure all interactions involving a specified set of core nodes, which produces a graph containing this core together with a potentially larger set of fringe nodes that link to the core. Interactions between nodes in the fringe, however, are not present in the resulting graph data. For example, a phone service provider may only record calls in which at least one of the participants is a customer; this can include calls between a customer and a non-customer, but not between pairs of non-customers. Knowledge of which nodes belong to the core is crucial for interpreting the dataset, but this metadata is unavailable in many cases, either because it has been lost due to difficulties in data provenance, or because the network consists of "found data" obtained in settings such as counter-surveillance. This leads to an algorithmic problem of recovering the core set. Since the core is a vertex cover, we essentially have a planted vertex cover problem, but with an arbitrary underlying graph. We develop a framework for analyzing this planted vertex cover problem, based on the theory of fixed-parameter tractability, together with algorithms for recovering the core. Our algorithms are fast, simple to implement, and out-perform several baselines based on core-periphery structure on various real-world datasets.

A typical way in which network data is recorded is to measure all interactions involving a specified set of core nodes, which produces a graph containing this core together with a potentially larger set of fringe nodes that link to the core. Interactions between nodes in the fringe, however, are not present in the resulting graph data. For example, a phone service provider may only record calls in which at least one of the participants is a customer; this can include calls between a customer and a non-customer, but not between pairs of non-customers. Knowledge of which nodes belong to the core is crucial for interpreting the dataset, but this metadata is unavailable in many cases, either because it has been lost due to difficulties in data provenance, or because the network consists of "found data" obtained in settings such as counter-surveillance. This leads to an algorithmic problem of recovering the core set. Since the core is a vertex cover, we essentially have a planted vertex cover problem, but with an arbitrary underlying graph. We develop a framework for analyzing this planted vertex cover problem, based on the theory of fixed-parameter tractability, together with algorithms for recovering the core. Our algorithms are fast, simple to implement, and out-perform several baselines based on core-periphery structure on various real-world datasets.