Verdict lists the top five terms tweeted on IoT in Q1 2020, based on data from GlobalData's Influencer Platform. The top tweeted terms are the trending industry discussions happening on Twitter by key individuals (influencers) as tracked by the platform. Tech innovations in the form of robotics, automated parking, and more, the use cases of artificial intelligence across industries such as retail, automotive, and smart cities, were popularly discussed in Q1 2020. According to a video shared by Evan Kirstel, a top B2B influencer, buyers can shop for they wanted at stores in just 14 seconds, much like the Amazon Go stores that allows cashierless shopping by infusing sensors all over the stores that tracks what customers maybe buying after scanning the app, after which buyers just walk away with their products. Mike Quindazzi, a digital alliances sales leader, further shared a video on new automated parking that saves both time and space.

Appropriate allocation of system resources is essential for meeting the increased user-traffic demands in the next generation wireless technologies. Traditionally, the system relies on channel state information (CSI) of the users for optimizing the resource allocation, which becomes costly for fast-varying channel conditions. Considering that future wireless technologies will be based on dense network deployment, where the mobile terminals are in line-of-sight of the transmitters, the position information of terminals provides an alternative to estimate the channel condition. In this work, we propose a coordinates-based resource allocation scheme using supervised machine learning techniques, and investigate how efficiently this scheme performs in comparison to the traditional approach under various propagation conditions. We consider a simplistic system set up as a first step, where a single transmitter serves a single mobile user. The performance results show that the coordinates-based resource allocation scheme achieves a performance very close to the CSI-based scheme, even when the available coordinates of terminals are erroneous. The proposed scheme performs consistently well with realistic-system simulation, requiring only 4 s of training time, and the appropriate resource allocation is predicted in less than 90 microseconds with a learnt model of size less than 1 kB.

Machine Learning for graphs is nowadays a research topic of consolidated relevance. Common approaches in the field typically resort to complex deep neural network architectures and demanding training algorithms, highlighting the need for more efficient solutions. The class of Reservoir Computing (RC) models can play an important role in this context, enabling to develop fruitful graph embeddings through untrained recursive architectures. In this paper, we study progressive simplifications to the design strategy of RC neural networks for graphs. Our core proposal is based on shaping the organization of the hidden neurons to follow a ring topology. Experimental results on graph classification tasks indicate that ring-reservoirs architectures enable particularly effective network configurations, showing consistent advantages in terms of predictive performance.

The new report on the global Artificial Intelligence in the Telecommunication market is an extensive study on the overall prospects of the Artificial Intelligence in the Telecommunication market over the assessment period. Further, the report provides a thorough understanding of the key dynamics of the Artificial Intelligence in the Telecommunication market including the current trends, opportunities, drivers, and restraints. The report introspects the micro and macro-economic factors that are expected to nurture the growth of the Artificial Intelligence in the Telecommunication market in the upcoming years and the impact of the COVID-19 pandemic on the Artificial Intelligence in the Telecommunication . In addition, the report offers valuable insights pertaining to the supply chain challenges market players are likely to face in the upcoming months and solutions to tackle the same. The report suggests that the global Artificial Intelligence in the Telecommunication market is projected to reach a value of US$XX by the end of 2029 and grow at a CAGR of XX% through the forecast period (2019-2029).

When it comes to the 5G roll-out, AI will definitely be supercharged. "AI is a huge priority," said John Smee, who is the VP of engineering and head of 5G R&D for Qualcomm. "We are seeing at transformation happening, with AI going from the cloud to being distributed, such as on the edge or IoT devices." In preparation for this, Qualcomm has been embedding AI capabilities on its chips. Note that its AI engine has applications for cameras, battery life, security and gaming--allowing for neural network processing.

When it comes to the 5G roll-out, AI will definitely be supercharged. "AI is a huge priority," said John Smee, who is the VP of engineering and head of 5G R&D for Qualcomm. "We are seeing at transformation happening, with AI going from the cloud to being distributed, such as on the edge or IoT devices." In preparation for this, Qualcomm has been embedding AI capabilities on its chips. Note that its AI engine has applications for cameras, battery life, security and gaming--allowing for neural network processing.

LONDON (April 29, 2020) -- Global AI software revenue is projected to increase by 12-fold in the next seven years, skyrocketing from $10.1 billion in 2018 to $126.0 billion in 2025 according to Omdia. "The global AI market is entering a new phase in 2020 where the narrative is shifting from asking whether AI is viable to declaring that AI is now a requirement for most enterprises that are trying to compete on a global level," said Keith Kirkpatrick, principal analyst with Omdia. "AI is likely to trigger major transformations in industries where there is a clear case for incorporating AI, rather than in pie-in-the-sky use cases that may not generate a return on investment (ROI) for many years." Top use cases Demand for artificial intelligence in the consumer, enterprise, government and defense sectors is growing. Currently, the number of business-to-business (B2B) software opportunities related to AI total 333 and cover 28 industry sectors, with 203 unique use cases.

SoftBank Corp.'s fifth-generation wireless service in Japan is living up to the hype in at least one respect -- internet speeds that are blazingly fast even by the standards of one of the most connected countries in the world. The carrier's month-old 5G network topped out at 1.1 gigabits per second for downloads and about 30 megabits for uploads in tests carried out by Bloomberg News in Tokyo. Speeds of this kind, far surpassing typical wired broadband connections, have previously been possible only by pushing a fiber optic cable directly into a user's home. But there are significant pieces still missing and preventing mass adoption: Coverage is severely limited for now, there's little in the way of appealing content to capitalize on all that extra bandwidth and mobile data plans have yet to be revised to account for the much-increased consumption that 5G portends. SoftBank and local rivals KDDI Corp. and NTT Docomo Inc. all launched their 5G offerings in late March in a handful of metropolitan areas around the country, while newcomer Rakuten Inc. has targeted June for launch.

Intelligence is groomed by information and it is how the information is given in that determines the intensity of artificial intelligence. Given the fact that we communicate with languages and languages serve as the epicenter of conveying our ideas, Language Processing as conversational as it can be is one of the things that makes artificial intelligence more interesting. This aspect, known as Natural Language Processing or NLP is a subset of artificial intelligence. Natural Language Processing can teach the Machines to listen to human conversations in a practical way and comprehend the meaning out of it in a logical or technical way. Natural Language Processing, if applied accurately, can help in a variety of industries and segments including retail, telecommunication, E-Commerce and in various regions like sales, support, and marketing.

Nothing is more comforting than being greeted by your favorite drink just as you walk through the door of the corner café. While a thoughtful barista knows you take a macchiato every Wednesday morning at 8:15, it's much more difficult in a digital space for your preferred brands to personalize your experience. Talking Data, China's largest third-party mobile data platform, understands that everyday choices and behaviors paint a picture of who we are and what we value. Currently, Talking Data is seeking to leverage behavioral data from more than 70% of the 500 million mobile devices active daily in China to help its clients better understand and interact with their audiences. So, the business problem is to predict the demographic characteristics of the users using their app usage,geographical location and device properties.