Although both share a lot of the same features, like a display notch, the Huawei P20 stands out with the inclusion of a triple-camera setup on its back. The Huawei P20 features a 5.9-inch LCD display, while the P20 Pro comes with a slightly bigger 6.3-inch OLED display. Both have the same screen resolution of 2,240 x 1,080 and a tall aspect ratio of 18.7:9. Since Apple popularized the notch with the iPhone X, Huawei is following suit by putting smaller notches on both the P20 and P20 Pro. The difference here however is that users can "remove" the notch through software.

Huawei's latest smartphone can take photos in near-dark conditions without using its flash or a tripod. The P20 Pro takes exposures lasting up to six seconds to get enough light. It then uses artificial intelligence to deliver sharp images and avoid the blurring and smearing normally associated with employing this technique handheld. The Chinese company recently told the BBC it could soon become the world's bestselling smartphone brand. At present, it is in third place behind Samsung and Apple, with US telecom networks' refusal to sell its handsets proving an obstacle.

Huawei may be best known for US retailers not stocking its wares, but regardless, the company continues to ramp up its flagship smartphones. In the past few years, phones like the P9 made a lot of us stand up and take notice, thanks to classy design touches and Huawei's own imaging tricks. Its next phones, the P20 and P20 Pro, take that latter part even further as the company tries to spar with Samsung and the rest with a tapestry of AI skills and so very many camera sensors. There's so much going on when it comes to imaging (both in terms of hardware and software) that, at least during my short time with both phones, I couldn't test out all the modes and use cases. I'll say this, though: Huawei is taking its smartphone cameras very seriously.

We propose a method to improve the radio link performance in a wireless network using a deep Q-Learning based algorithm. In this paper, we use this reinforcement learning model to allow the wireless network cluster to self-heal by performing certain fault management actions which improves the radio link performance of this wireless network. The main contributions of this paper are: 1) introduce a radio performance tuning algorithm that self-organizing networks can implement in a polynomial runtime, 2) employ deep reinforcement learning to perform fault management, and 3) show that this fault management method can improve the radio link performance in a realistic network setup. Simulation results show that an optimal action sequence to clear alarms is feasible even against the randomness of the network faults and user movements.

Employers will face a crisis hiring and developing skilled staff as artificial intelligence (AI) begins to shake up the structure of the workforce. Every conference this year contains a dead human genius reincarnated as software system or a robot. Yes, there is a lot of hype, but there is real worth in AI and Machine Learning. Read our counseling on how to avoid adopting "black box" approach. You forgot to provide an Email Address.

Not surprisingly, this year's smartphones feature faster processors than those from last year--that happens every year. But what is new this year is the predominance of machine learning features that just about every processor vendor is touting as a way of differentiating their devices. This is true for the phone vendors who design their own chips, the independent or merchant chip vendors who sell processors to phone vendors, and even the IP makers who design the cores that go into the processors themselves. First a little background: all modern application processors include designs (often referred to as intellectual property, or IP) from other companies, notably firms like ARM, Imagination Technologies, MIPS, and Ceva. Such IP can appear in various forms--for example, ARM sells everything from a basic license for its 32-bit and 64-bit architecture, to specific cores for CPUs, graphics, image processing, etc., that chip designers can then use to create processors.

Last week, telecommunications experts gathered in Barcelona for Mobile World Congress, the industry's premier event and a chance for tech companies to show off their latest innovations in areas as diverse as 5G technology, artificial intelligence, and the cloud. Although the cloud is far from a new idea, its true capabilities are only now beginning to be realized. Here are four ways in which the cloud will shape our lives over the next decade and beyond. Cloud will provide the digital infrastructure of tomorrow's cities, where an estimated 6 billion of the world's population will live by 2045. Smart elevators and parking lots, driverless cars and drone taxis, trains and subways, farms and power plants – all will be safer and better managed, thanks to the cloud's ability to store and analyze data.

With more than 4 billion internet users worldwide today and 31 billion connected devices forecasted by 2020, the future of the digital world lies in how people and "things" will interact with each other. The key to this will be the convergence and consolidation of internet of things platforms and devices which will be able to seamlessly exchange data between people, networks, devices and applications. Creating this world, where multiple service and technology layers work harmoniously to create ubiquitous, ultra-connected experiences, is a task that will take years to complete. It requires a robust technology platform, powered by artificial intelligence. Today, we are siloed in how we think about IoT.

Zero Touch & Carrier Automation Congress -- The 3GPP standards group is developing a machine learning function that could allow 5G operators to monitor the status of a network slice or third-party application performance. The network data analytics function (NWDAF) forms a part of the 3GPP's 5G standardization efforts and could become a central point for analytics in the 5G core network, said Serge Manning, a senior technology strategist at Sprint Corp. (NYSE: S). Speaking here in Madrid, Manning said the NWDAF was still in the "early stages" of standardization but could become "an interesting place for innovation." The 3rd Generation Partnership Project (3GPP) froze the specifications for a 5G new radio standard at the end of 2017 and is due to freeze another set of 5G specifications, covering some of the core network and non-radio features, in June this year as part of its "Release 15" update. Manning says that Release 15 considers the network slice selection function (NSSF) and the policy control function (PCF) as potential "consumers" of the NWDAF.

Deep neural networks are often trained in the over-parametrized regime (i.e. with far more parameters than training examples), and understanding why the training converges to solutions that generalize remains an open problem. Several studies have highlighted the fact that the training procedure, i.e. mini-batch Stochastic Gradient Descent (SGD) leads to solutions that have specific properties in the loss landscape. However, even with plain Gradient Descent (GD) the solutions found in the over-parametrized regime are pretty good and this phenomenon is poorly understood. We propose an analysis of this behavior for feedforward networks with a ReLU activation function under the assumption of small initialization and learning rate and uncover a quantization effect: The weight vectors tend to concentrate at a small number of directions determined by the input data. As a consequence, we show that for given input data there are only finitely many, "simple" functions that can be obtained, independent of the network size. This puts these functions in analogy to linear interpolations (for given input data there are finitely many triangulations, which each determine a function by linear interpolation). We ask whether this analogy extends to the generalization properties - while the usual distribution-independent generalization property does not hold, it could be that for e.g. smooth functions with bounded second derivative an approximation property holds which could "explain" generalization of networks (of unbounded size) to unseen inputs.