AI is rapidly transforming the way that companies interact with their customers. MIT Technology Review Insights' survey of 1,004 business leaders, "The global AI agenda," found that customer service is the most active department for AI deployment today. By 2022, it will remain the leading area of AI use in companies (say 73% of respondents), followed by sales and marketing (59%), a part of the business that just a third of surveyed executives had tapped into as of 2019. In recent years, companies have invested in customer service AI primarily to improve efficiency, by decreasing call processing and complaint resolution times. Organizations known as leaders in the customer experience field have also looked toward AI to increase intimacy--to bring a deeper level of customer understanding, drive customization, and create personalized journeys.

The artificial intelligence (AI) is an advanced technology that has decision-making ability.

Last week, Qualcomm announced the Snapdragon 845, which sends AI tasks to the most suitable cores. There's not a lot of difference between the three company's approaches -- it ultimately boils down to the level of access each company offers to developers and how much power each setup consumes. Before we get into that though, let's figure out if an AI chip is really all that different from existing CPUs. A term you'll hear a lot in the industry with reference to AI lately is "heterogeneous computing." It refers to systems that use multiple types of processors, each with specialized functions, to gain performance or save energy.

As a mature 5G application scenario, video will embrace more promising prospects in the future. With 5G networks, high-quality video content such as UHD, 4K, 8K, and 120-frame videos will become popular for consumers, and VR, AR, interactive video, and AI-based video content will be their next hot videos. Paid video content will also change in intelligent distribution mode such as advertising, and the use of big data and AI can make video content target the right audience, which will improve video revenues. As 5G is widely adopted, there will be more video applications such as 4K/8K videos, VR immersive experience, AR, ultra-low latency live broadcast, high-speed mobile video communication, mobile communication in a crowded environment, as well as multimedia and IoV. In the 5G era, UHD video and ultra-high speed will greatly meet people's daily video viewing needs.

On the surface, it would seem that artificial intelligence (AI) is widespread in the telecom industry. For years we've been familiar with voice-activated menu systems that respond to your verbal commands. However, the potential for AI in the telecom arena goes much deeper than voice controls, albeit with some unique challenges. Scott Matteson: What are the opportunities for AI in the telecom space? Tom Footit: Telecom networks generate an enormous amount of data, and as a result there are a lot of opportunities for AI in this space.

The ever-increasing demand for media streaming together with limited backhaul capacity renders developing efficient file-delivery methods imperative. One such method is femto-caching, which, despite its great potential, imposes several challenges such as efficient resource management. We study a resource allocation problem for joint caching and transmission in small cell networks, where the system operates in two consecutive phases: (i) cache placement, and (ii) joint file- and transmit power selection followed by broadcasting. We define the utility of every small base station in terms of the number of successful reconstructions per unit of transmission power. We then formulate the problem as to select a file from the cache together with a transmission power level for every broadcast round so that the accumulated utility over the horizon is maximized. The former problem boils down to a stochastic knapsack problem, and we cast the latter as a multi-armed bandit problem. We develop a solution to each problem and provide theoretical and numerical evaluations. In contrast to the state-of-the-art research, the proposed approach is especially suitable for networks with time-variant statistical properties. Moreover, it is applicable and operates well even when no initial information about the statistical characteristics of the random parameters such as file popularity and channel quality is available.

Deep Tech is a new subscriber-only podcast that brings alive the people and ideas in our print magazine. Episodes are released every two weeks. We're making the first four installments, built around our 10 Breakthrough Technologies issue, available for free. Every two weeks, give or take, SpaceX puts another 60 Starlink communications satellites into low Earth orbit. Its initial goal is to launch 12,000 of these small mass-produced satellites--six times the number of operating satellites currently in orbit--with another 42,000 possibly to follow. Other companies such as Amazon, Telesat, and Planet are planning their own satellite "mega-constellations." The result could be a welter of new space-based services, from Internet connectivity to continuous mapping. But there's also growing attention to the potential downsides, including an increased risk of collisions that could end up littering low Earth orbit with dangerous debris and rendering it unusable. In this episode of Deep Tech, we hear from OneWeb founder Greg Wyler and science writer and former astrophysicist Ramin Skibba about efforts to mitigate the hazards.

Diagnosing problems in Internet-scale services remains particularly difficult and costly for both content providers and ISPs. Because the Internet is decentralized, the cause of such problems might lie anywhere between an end-user's device and the service datacenters. Further, the set of possible problems and causes is not known in advance, making it impossible in practice to train a classifier with all combinations of problems, causes and locations. In this paper, we explore how different machine learning techniques can be used for Internet-scale root cause analysis using measurements taken from end-user devices. We show how to build generic models that (i) are agnostic to the underlying network topology, (ii) do not require to define the full set of possible causes during training, and (iii) can be quickly adapted to diagnose new services. Our solution, DiagNet, adapts concepts from image processing research to handle network and system metrics. We evaluate DiagNet with a multi-cloud deployment of online services with injected faults and emulated clients with automated browsers. We demonstrate promising root cause analysis capabilities, with a recall of 73.9% including causes only being introduced at inference time.

Wireless operators spend millions of dollars every year paying for the electricity to power their cell sites and small cells. But there are new energy-saving features that are being developed that could make a dramatic difference in energy consumption. And these new features incorporate tools like artificial intelligence (AI) and machine learning. In a new Ericsson white paper called "Breaking the Energy Curve," the company said that machine learning can be used to make certain network features more autonomous. Two of those features, MIMO Sleep Mode and Cell Sleep Mode, are using machine learning to study data traffic patterns and save operators money.

Hexa Food's IoT team has deployed Huawei's ModelArts coupled with the intelligent device Atlas 500 to accurately identify the quality of the chilies it uses in spice blends. The AI can distinguish good chilies from bad, improving production efficiency and the quality of spices available to chefs and homes across Malaysia. Known as the "Kingdom of Spices", Malaysia is a multi-ethnic nation comprising Malays, Chinese, Indians, and the indigenous Orang Asli people. Its diverse culture is reflected in its cuisine, which draws from a multicultural heritage that sees hundreds of spices add flavor to the Malaysian diet. And of these, the colorful, aromatic, and spicy chili powder is a mainstay of many of the nation's signature dishes.