Coding theory is a central discipline underpinning wireline and wireless modems that are the workhorses of the information age. Progress in coding theory is largely driven by individual human ingenuity with sporadic breakthroughs over the past century. In this paper we study whether it is possible to automate the discovery of decoding algorithms via deep learning. We study a family of sequential codes parameterized by recurrent neural network (RNN) architectures. We show that creatively designed and trained RNN architectures can decode well known sequential codes such as the convolutional and turbo codes with close to optimal performance on the additive white Gaussian noise (AWGN) channel, which itself is achieved by breakthrough algorithms of our times (Viterbi and BCJR decoders, representing dynamic programing and forward-backward algorithms). We show strong generalizations, i.e., we train at a specific signal to noise ratio and block length but test at a wide range of these quantities, as well as robustness and adaptivity to deviations from the AWGN setting.

The Mobile Virtual Network Operator (MVNO) business model first emerged in Japan in 1997. Since then, the global MVNO subscriber base has steadily grown and is expected to soon exceed the 300-million landmark. It is currently growing five times faster than the operator segment. The MVNO business market has however, always been controversial. Despite its success, many MVNOs struggle financially and many fail a few months after their much-hyped launch.

As pop-up stores around the world put the latest OnePlus handset on sale today, and the rest of the world's retail stores put it on sale tomorrow, what does the world's press think of the OnePlus 6? The notch is on show, the dual lens camera is tested, and the all-glass construction help it stand out. is that enough? OnePlus is leaning heavily on "The Speed You Need" as the key marketing phrase, and the specifications bear that out. Much like many high-end handsets, the OnePlus 6 comes with a Qualcomm Snapdragon 845, paired up with 6gB or 8GB of RAM, and internal storage options of 64GB, 128GB, or 256GB. I still think that getting the 8GB RAM model is unnecessary for most customers, and the Pixel 2 XL shows that there is still room for improvement when it comes to smoothness.

Artificial Intelligence (AI) and Machine Learning (ML) are two buzzing terms that you are most likely to hear almost every day. Right from Google and Amazon to Microsoft, Huawei and Samsung are all deploying AI in their products. Now, Tecno Mobile, the Chinese smartphone company is all set to launch a new selfie-centric smartphone in India in its Camon-series. BGR India has learned that the key highlight of the smartphone will be its AI-enabled selfie camera. The smartphone will leverage AI technology to make you look good in your selfies.

In this paper we consider whether RNNs can learn functionally equivalent models of unknown computer hardware peripherals through input/output observation. Peripheral devices attach to a main computer and use both hardware within the device and driver software running on the main computer to perform a task, such as printing a page or sending a message. However, there are instances when hardware is accessible from the main system but driver software is not, rendering the peripheral unusable. This situation is prevalent in open source operating systems where driver software may not be available from the vendor. Without driver software or development documentation, it is incumbent on the system's owner to write software to make use of the peripheral. The device itself is a "black box", with no information directly available to the developer beyond a set of memory addresses to interact with the device and the observable output of the hardware itself. This leads to labor-intensive reverse engineering efforts with varying degrees of success (see e.g.

The ubiquitous complex networks are often composed of regular and irregular components, which makes uncovering the complexity of network structure into a fundamental challenge in network science. Exploring the regular information and identifying the roles of microscopic elements in network organization can help practitioners to recognize the universal principles of network formation and facilitate network data mining.Despite many algorithms having been proposed for link prediction and network reconstruction, estimating and regulating the reconstructability of complex networks remains an inadequately explored problem. With the practical assumption that there has consistence between local structures of networks and the corresponding adjacency matrices are approximately low rank, we obtain a self-representation network model in which the organization principles of networks are captured by representation matrix. According to the model, original networks can be reconstructed based on observed structure. What's more, the model enables us to estimate to what extent networks are regulable, in other words, measure the reconstructability of complex networks. In addition, the model enables us to measure the importance of network links for network regularity thereby allowing us to regulate the reconstructability of networks. The extensive experiments on disparate networks demonstrate the effectiveness of the proposed algorithm and measure. Specifically, the structural regularity reflects the reconstructability of networks, and the reconstruction accuracy can be promoted via the deleting of irregular network links independent of specific algorithms.

An Information Communication Technology (ICT) expert, Mr Adede Williams, says the introduction of Artificial Intelligence (AI) will ease the difficulty involved in doing businesses in the country. Williams, who is President of Association of Telecommunications Professionals of Nigeria (APTN), stated this in an interview with on Thursday in Abuja. He spoke at the backdrop of the 2018 World Telecommunication and Information Society Day, with "Enabling the Positive Use of Artificial Intelligence for All'' as theme. Artificial Intelligence is an aspect of Computer Science that emphasizes the creation of intelligent machines that work and react, like humans. Williams said that the theme for the year was apt and carefully selected by the International Telecommunication Union (ITU), adding that the positive use of AI was a powerful message to the public. He said that AI could be used in all sectors of the economy as it made the job easier and effective. "When you look at what is happening these days, artificial intelligence is what is actually taking care of a lot of things.

TouchPal, a very popular AI-powered virtual keyboard for Android devices (with more than 700m users) recently unveiled something called Talia, a voice-activated intelligent assistant. While I prefer to live in the physical keyboard world (hello BlackBerry KEY2), using a virtual keyboard is pretty much inevitable if you have a modern smartphone. TouchPal pairs Android keyboards to the functionality of all the apps we addictively use on the daily: Facebook, Instagram, Snap, LinkedIn, YouTube, Chrome, and so on. TouchPal learns from user input to personalize and proactively tailor recommendations, suggestions and custom content. It is also an ad-supported network and requires a ton of permissions in order to operate on your phone. Considering how much it needs to know in order to make your life (I guess) more streamlined, this makes sense.

Japan-based mobile operator SoftBank improved its radio access design in the Tokai region by using a network automation service from Ericsson. Ericsson's elastic radio access (RAN) design uses machine learning and big data analytics to enable automation. The end result is an improved user experience and a reduction in lead time of 40% over traditional network design methods. While SoftBank is seeing improved benefits now with the new design, which it will use in other geographical areas, automation and the use of artificial intelligence will have a big impact in the near future on the rollout of 5G technologies and designs. "I would say that this is a solid incremental step toward planning and operating much denser radio networks, which 5G will certainly need," said IDC's Andy Hicks, research director, EMEA telecommunications and networking.

Japanese technology giant SoftBank has partnered with Swedish telecoms provider Ericsson to trial new radio technology that uses machine learning to design networks and improve performance. SoftBank, whose investments in emerging technologies include Uber and robotics specialist Boston Dynamics, is a leading mobile network operator in Japan and one of the country's largest organisations. The company has been implementing Ericsson's machine intelligence method for advanced radio network design in the Tokai region of the country. Ericsson's technology relies on a thorough analysis of the radio network environment. This includes assessing cell coverage overlap, signal strength, and receiver diversity.