Axios recently reported that it had discovered a document that revealed something very interesting: The Trump administration was considering a government-run 5G network. According to the memo, this was in order to fight China's upcoming dominance in the wireless 5G space, and would ensure a safe network for self-driving cars, AI, VR and other cutting-edge technologies. This kind of state-run network is completely antithetical to the administration's public stance on deregulation and privatization. It even prompted FCC Chairman Ajit Pai to come down strongly in opposition. It turns out, however, that the document was outdated, and the Trump administration strongly denies it ever seriously considered such a proposal.

Artificial Intelligence (AI) is being groomed, says Rethink Technology Research to partner with self-optimising networks (SON) to create cellular networks that know the user, perform brilliantly and defy complexity. But 50% of network providers worry they will not be able to attract sufficient AI skills. While everyone today is aware of the race to build a self-driving car, the race to build a self-driving cellular network is far less of a public property, but nonetheless, it is a race that is critical to the survival of existing mobile network operators (MNOs). The rise in self-optimising networks, along with a huge increase in the total number of cells, will lead to a radical uptake of virtualised Software Defined Networks. This will make it possible to dial network resources up and down on-demand, but only if MNOs can cope with another level of network complexity.

Recent Galaxy S9 leaks, many coming from the ever-reliable Evan Blass, tell us pretty much all there is to know about Samsung's upcoming flagship: It'll have a better chip, a better camera, and a repositioned fingerprint sensor. The S9 doesn't look groundbreaking, but it should be a nice upgrade for people still rocking an S7. But this being the smartphone wars, Samsung can't just let the iPhone X go unnoticed. While it's almost certain the S9 won't have a 3D sensing camera like Apple's flagship phone, a new feature hidden inside Samsung's Oreo beta called Intelligent Scan shines light on a new biometric that many will compare to Face ID. Intelligent Scan reportedly combines iris and face scanning to "to improve accuracy and security even in low or very bright light," according to messages hidden in the Oreo beta.

Samsung has hinted that the Galaxy S9 might include more advanced face recognition, but we're now getting clues as to what's involved. SamCentral's sleuthing in the settings APK for the Galaxy Note 8's Oreo beta has discovered a hidden Intelligent Scan feature that uses both camera-based face detection and the iris scanner in tandem for "better accuracy and security" and improved results in "low or very bright" lighting. Given that the iris scanning on the S8 and Note 8 can be finnicky, this could deliver a much more consistent experience when you're unlocking your phone or accessing secure info. Just how it works isn't immediately apparent. A video included with the feature suggests that both the iris scanner and camera are active at the same time regardless of the conditions, but it's not certain whether this means combining their data or using one as a backup for the other.

Management teams often assume they can leapfrog best practices for basic data analytics by going directly to adopting artificial intelligence and other advanced technologies. But companies that rush into sophisticated artificial intelligence before reaching a critical mass of automated processes and structured analytics can end up paralyzed. They can become saddled with expensive start-up partnerships, impenetrable black-box systems, cumbersome cloud computational clusters, and open-source toolkits without programmers to write code for them. By contrast, companies with strong basic analytics -- such as sales data and market trends -- make breakthroughs in complex and critical areas after layering in artificial intelligence. For example, one telecommunications company we worked with can now predict with 75 times more accuracy whether its customers are about to bolt using machine learning.

Startup Gyrfalcon is moving fast with a chip for inferencing on deep neural networks, but it faces an increasingly crowded market in AI silicon. A year after it got its first funding, the company is showing a working chip and claiming design wins in smartphones, security cameras and industrial automation equipment. Data centers typically train deep neural networks and run inference tasks on them using banks of servers. Increasingly, client and embedded systems from cars to handsets are adopting accelerators to speed the inferencing jobs. Apple, Google and Huawei are already shipping smartphones with inferencing blocks in their custom SoCs.

Passively mobile communicating machines that use restricted space.

As new technologies are discovered and developed, widespread adoption doesn't occur until viable business benefits can be identified and validated. Machine Learning, Cognitive Computing or Artificial Intelligence (depending on what you call it) is a "hot," interesting new technology development, and one that is quickly proceeding through the hype cycle to widespread adoption. As a practical use case, Machine Learning can now be used to gain new, perhaps even unexpected insights into sales team engagement to predict sales turnover. Based on the Harvard Business Review, the rate of annual sales turnover among U.S. salespeople is as high as 27%--a rate that twice the average of the overall labor force. As employees pursue new employment, their replacements must be hired and trained, costing time, effort and resources.

Enterprise machine learning pilots and deployments are expected to double this year and smartphone adoption will continue to experience a significant increase. This is according to Deloitte Global's 17th edition of the Technology, Media & Telecommunications (TMT) Predictions research. The report predicts that global organisations will double their use of machine learning technology by the end of 2018 and smartphone sales are expected to double, with more than 90% of adults in developed countries expected to have a smartphone by the end of 2023. Enterprise machine learning pilots and deployments are predicted to double this year. TMT predictions highlights some key areas that Deloitte Global believes will unlock more intensive use of machine learning in the enterprise by making it easier, cheaper and faster.

You will be spoiled for choice when looking for stocks to take advantage of the booming artificial intelligence (AI) market. Almost all the well-known tech giants -- including NVIDIA, Intel, Amazon, Alphabet, and many others -- are betting big on this fast-growing field, as they don't want to miss out on an opportunity that could be worth a total of almost $60 billion by 2025. But these aren't the only ways to take advantage of this space. Lesser-known stocks like Xilinx (NASDAQ:XLNX), Ciena (NYSE:CIEN), and CEVA (NASDAQ:CEVA) could win big from the AI revolution. Xilinx makes programmable logic devices used across several growth segments such as data centers, automotive, and industrial.