Telefonica has become one of the first major technology companies to reveal exactly what guidelines it will place on the use of AI technology. The Spanish telecoms giant and parent company of O2 has released its Principles of Artificial Intelligence, laying out how it plans to utilise the technology to ensure a positive impact on society. Promising "integrity and transparency" in its design and development of AI technology, Telefonica's rules include the company's ongoing stance on areas such as equality, transparency, clarity, privacy, and security. "We're concerned about the possible use of artificial intelligence for the creation or dissemination of fake news, addiction to technology, and the possible reinforcement of social bias in the algorithms in general", said José María Álvarez-Pallete, Chairman & CEO of Telefónica. "These phenomena undermine the trust of our customers, our most valuable asset, and hinder the development of a fairer society. Consequently, we will do everything in our power to collaborate with other entities in order to eradicate them", he adds.

Huawei has made really good phones for years, but the Mate 20 Pro is the Chinese firm's first truly cutting-edge device with a triple camera, 3D face unlock and an in-screen fingerprint sensor. The Mate series of phones has always delivered one thing above all else – battery life. This year Huawei has gone out of its way to deliver even more. The Mate 20 Pro is the best feeling, most premium device the Chinese firm has made. It's incredibly solid, smooth and well built, but at 189g is still surprisingly light and manageable compared to the 208g iPhone XS Max and 201g Samsung Galaxy Note 9.

Graph neural networks (GNNs) have been shown to replicate convolutional neural networks' (CNNs) superior performance in many problems involving graphs. By replacing regular convolutions with linear shift-invariant graph filters (LSI-GFs), GNNs take into account the (irregular) structure of the graph and provide meaningful representations of network data. However, LSI-GFs fail to encode local nonlinear graph signal behavior, and so do regular activation functions, which are nonlinear but pointwise. To address this issue, we propose median activation functions with support on graph neighborhoods instead of individual nodes. A GNN architecture with a trainable multirresolution version of this activation function is then tested on synthetic and real-word datasets, where we show that median activation functions can improve GNN capacity with marginal increase in complexity.

The world is witnessing an unprecedented growth of cyber-physical systems (CPS), which are foreseen to revolutionize our world {via} creating new services and applications in a variety of sectors such as environmental monitoring, mobile-health systems, intelligent transportation systems and so on. The {information and communication technology }(ICT) sector is experiencing a significant growth in { data} traffic, driven by the widespread usage of smartphones, tablets and video streaming, along with the significant growth of sensors deployments that are anticipated in the near future. {It} is expected to outstandingly increase the growth rate of raw sensed data. In this paper, we present the CPS taxonomy {via} providing a broad overview of data collection, storage, access, processing and analysis. Compared with other survey papers, this is the first panoramic survey on big data for CPS, where our objective is to provide a panoramic summary of different CPS aspects. Furthermore, CPS {require} cybersecurity to protect {them} against malicious attacks and unauthorized intrusion, which {become} a challenge with the enormous amount of data that is continuously being generated in the network. {Thus, we also} provide an overview of the different security solutions proposed for CPS big data storage, access and analytics. We also discuss big data meeting green challenges in the contexts of CPS.

Editor's note: The use of AI in LinkedIn products has been the subject of multiple press articles and research papers (some highlighted on this blog). With the release of a new LinkedIn Learning course about AI at LinkedIn, we asked our Head of AI, Deepak Agarwal, for a brief overview of what AI is and how it works, geared towards people who are interested in this growing field. In this post, we discuss AI as a broad topic and look at a few ways that it influences product design at LinkedIn. Back in 2005, I was working in my first job at AT&T Bell Labs. The telecommunications industry was struggling due to price wars and increased competition from wireless carriers.

Dynamic spectrum access (DSA) is regarded as an effective and efficient technology to share radio spectrum among different networks. As a secondary user (SU), a DSA device will face two critical problems: avoiding causing harmful interference to primary users (PUs), and conducting effective interference coordination with other secondary users. These two problems become even more challenging for a distributed DSA network where there is no centralized controllers for SUs. In this paper, we investigate communication strategies of a distributive DSA network under the presence of spectrum sensing errors. To be specific, we apply the powerful machine learning tool, deep reinforcement learning (DRL), for SUs to learn "appropriate" spectrum access strategies in a distributed fashion assuming NO knowledge of the underlying system statistics. Furthermore, a special type of recurrent neural network (RNN), called the reservoir computing (RC), is utilized to realize DRL by taking advantage of the underlying temporal correlation of the DSA network. Using the introduced machine learning-based strategy, SUs could make spectrum access decisions distributedly relying only on their own current and past spectrum sensing outcomes. Through extensive experiments, our results suggest that the RC-based spectrum access strategy can help the SU to significantly reduce the chances of collision with PUs and other SUs. We also show that our scheme outperforms the myopic method which assumes the knowledge of system statistics, and converges faster than the Q-learning method when the number of channels is large.

You're going to hear a lot of claims about "5G firsts" as the leading telecom companies slug it out to deliver the next generation of wireless. And one just came from AT&T. The company says it has successfully completed the "world's first millimeter wave" 5G connection over a live network to what will be commercially available "standards-based" commercial mobile 5G device. Did your eyes just cross? Let's break this down a bit first.

Apple, Google, and Samsung all have smart speakers. Not to be left behind, fellow smartphone titan Huawei is playing catch up with another budget contender, following the reveal of its AI Cube (a speaker, 4G modem, WiFi router hybrid and Google Home clone, all rolled into one). The company teased the new gadget -- the Chinese name of which translates as "Huawei AI Speaker" -- at its Mate 20 series event in Shanghai, China, earlier today. As first impressions go, there's the glaringly obvious: this device looks like a HomePod doppelgänger, complete with a stout, cylindrical design with control buttons at the top. It also comes in black and white.

IDEAS 278,119 views SolidWorks Tutorial #181: Cola Bottle - Duration: 27:34. SolidWorks Tutorial 1,589,749 views Inside a Google data center - Duration: 5:28.

Machine learning made a big splash in 2018, and companies are expected to continue or increase their investments in this technology in the coming year. IDC forecasts that machine learning and AI spending will increase from $12 billion in 2017 to $57.6 billion by 2021. Data science platforms that support machine learning are predicted to grow at a 13% CAGR through 2021. Numerous industries have felt an enormous shift due to machine learning, and large tech giants continue to vie for top data science talent. Manufacturing, for example, saw the implementation of smart factories, where machines can essentially talk to each other and predictive analytics can forecast any potential problems.