Deloitte Global forecasts major strides in machine learning for the enterprise, a worldwide appetite for digital subscriptions among consumers, and ongoing smartphone dominance – along with eight additional predictions.Among the findings of the 17th edition of the Technology, Media & Telecommunications (TMT) Predictions are indications that business organisations will double their use of machine learning technology by the end of 2018. TMT Predictions highlights five key areas that Deloitte Global believes will unlock more intensive use of machine learning in the enterprise by making it easier, cheaper and faster. The most important key area is the growth in new semiconductor chips that will increase the use of machine learning, enabling applications to use less power, and at the same time become more responsive, flexible and capable. "We have reached the tipping point where adoption of machine learning in the enterprise is poised to accelerate," says Mark Casey, Deloitte global media & entertainment & TMT Africa leader. TMT Predictions includes a number of consumer forecasts as well.

Qualcomm is about to go in big with the burgeoning smart home scene. As well as its'Smart Audio Platform' CES announcement, which will help to push smart voice assistant technology into an even wider array of speakers, it's also looking to become a smart home hub gatekeeper in its own right. The Home Hub platform from Qualcomm will allow manufacturers to easily integrate the Google Assistant inside any smart device of their choosing. While one new Qualcomm system-on-a-chip focusses on appliances such as ovens and fridges, the second chipset is centered around the new wave of Google Assistant-powered devices that also feature a screen. As well as speakers like the Lenovo Smart Display pictured above, these will also include anything with a display, from thermostats to security systems.

It looks like Samsung's Galaxy S9 will come with a new feature to put it in direct competition with Apple's iPhone X.Credit: Tom's GuideThe tech giant has nearly completed the development of neural processing units (NPUs) to improve the artificial intelligence features baked into future smartphones, The Korea Herald is reporting, citing sources who claim to have knowledge of the chip's development. The Korea Herald's sources said Samsung is planning to add the artificial intelligence chips to both smartphones and servers. "For mobile devices, Samsung has already reached the technological levels of Apple and Huawei, but will come up with better chips for sure in the second half of the year," the source added. Like other companies, Samsung has been investing heavily in artificial intelligence on the premise that doing so could improve the broader user experience. Apple already offers an AI chip in its iPhone X that the company says can perform "up to 600 billion operations per second."

In this article a few simple applications of Markov chain are going to be discussed as a solution to a few text processing problems. These problems appeared as assignments in a few courses, the descriptions are taken straightaway from the courses themselves. Use a Markov chain to create a statistical model of a piece of English text. Simulate the Markov chain to generate stylized pseudo-random text. In the 1948 landmark paper A Mathematical Theory of Communication, Claude Shannon founded the field of information theory and revolutionized the telecommunications industry, laying the groundwork for today's Information Age. In this paper, Shannon proposed using a Markov chain to create a statistical model of the sequences of letters in a piece of English text. Markov chains are now widely used in speech recognition, handwriting recognition, information retrieval, data compression, and spam filtering. They also have many scientific computing applications including the genemark algorithm for gene prediction, the Metropolis algorithm for measuring thermodynamical properties, and Google's PageRank algorithm for Web search.

In this article a few simple applications of Markov chain are going to be discussed as a solution to a few text processing problems. These problems appeared as assignments in a few courses, the descriptions are taken straightaway from the courses themselves. Use a Markov chain to create a statistical model of a piece of English text. Simulate the Markov chain to generate stylized pseudo-random text. In the 1948 landmark paper A Mathematical Theory of Communication, Claude Shannon founded the field of information theory and revolutionized the telecommunications industry, laying the groundwork for today's Information Age. In this paper, Shannon proposed using a Markov chain to create a statistical model of the sequences of letters in a piece of English text. Markov chains are now widely used in speech recognition, handwriting recognition, information retrieval, data compression, and spam filtering. They also have many scientific computing applications including the genemark algorithm for gene prediction, the Metropolis algorithm for measuring thermodynamical properties, and Google's PageRank algorithm for Web search.

Artificial intelligence (AI) is becoming a bit of a buzzword these days, as companies in the industry make an effort to differentiate their offerings. In 2017, both Apple and Huawei released hardware designed to accelerating AI on their in-house chips -- Apple's A11 Bionic system-on-chip featured the Neural Engine, and Huawei's HiSilicon Kirin 970 had a Neural Processing Unit (NPU). Now, Samsung has nearly completed development of its own AI chips, according to a report by The Investor. The company is reportedly "almost done" with a hardware line optimized for servers, and it expects to commercialize it in the coming months. On the mobile device side of things, Samsung is said to have matched the technical achievements of Apple and Huawei.

In this paper, an interference-aware path planning scheme for a network of cellular-connected unmanned aerial vehicles (UAVs) is proposed. In particular, each UAV aims at achieving a tradeoff between maximizing energy efficiency and minimizing both wireless latency and the interference level caused on the ground network along its path. The problem is cast as a dynamic game among UAVs. To solve this game, a deep reinforcement learning algorithm, based on echo state network (ESN) cells, is proposed. The introduced deep ESN architecture is trained to allow each UAV to map each observation of the network state to an action, with the goal of minimizing a sequence of time-dependent utility functions. Each UAV uses ESN to learn its optimal path, transmission power level, and cell association vector at different locations along its path. The proposed algorithm is shown to reach a subgame perfect Nash equilibrium (SPNE) upon convergence. Moreover, an upper and lower bound for the altitude of the UAVs is derived thus reducing the computational complexity of the proposed algorithm. Simulation results show that the proposed scheme achieves better wireless latency per UAV and rate per ground user (UE) while requiring a number of steps that is comparable to a heuristic baseline that considers moving via the shortest distance towards the corresponding destinations. The results also show that the optimal altitude of the UAVs varies based on the ground network density and the UE data rate requirements and plays a vital role in minimizing the interference level on the ground UEs as well as the wireless transmission delay of the UAV.

Leading Canadian technology firm Blackberry has announced the expansion of its ongoing partnership with Qualcomm that will see the two companies collaborating on the development of technology for autonomous vehicles. "BlackBerry and Qualcomm Technologies have had a long-standing relationship for over a decade, collaborating on technologies that have revolutionized the way people live and work," said Sandeep Chennakeshu, President of BlackBerry Technology Solutions. "Today's announcement elevates our relationship as we aim to accelerate the delivery of the next generation platforms that connected vehicles will rely upon." Blackberry will integrate its QNX software with Qualcomm's hardware platforms that will then be used in several applications including virtual cockpit controllers, cellular vehicle-to-everything (C-V2X) technology and infotainment systems. "As innovation in the automotive industry accelerates it becomes necessary for industry leaders to work together to deliver leading-edge technology platforms that help to make vehicles safer, more connected, and increasingly autonomous," said Patrick Little, Senior Vice President and General Manager of Automotive, Qualcomm Technologies.

NEW DELHI: Chinese handset maker Huawei on Friday unveiled EMUI 8.0 -- its custom Operating System (OS) based on Android Oreo platform that promises to boost productivity with Artificial Intelligence (AI) capabilities. The EMUI 8.0, already available on Honor View 10, will be rolled out on devices such as Honor 8 Pro, Honor 9i, Honor 7X and Honor 8 Lite soon. "The update significantly cuts down on the number of steps needed to achieve the desired function through its smart applications like navigation dock and smart screen. It enables the user to reach 90 per cent of the core functions in a single-click," Huawei said in a statement. The custom OS engages in low-memory management by allocating resources in a way to provide more space to ensure holistic experience coupled with AI capabilities.

With Apple deciding to use new Depth Sensing technology for the iPhone X's Face ID, many believe that Android phone makers might start using it for their own devices. However, it looks like many Android flagships coming out during the first half of 2018 won't have the same 3D-sensing feature. Industry sources say that smartphone vendors, particularly in China, are actually more interested in selling the rest of their unsold inventories. This is also the reason why many of those Android phone manufacturers are postponing plans to develop new technologies like fast charging, wireless charging and Face ID-like features, according to Digitimes. Another reason that's hindering the adoption of 3D-sensing cameras on Android smartphones is the slow yield rates for producing components.