Artificial intelligence (AI) assisted unmanned aerial vehicle (UAV) aided next-generation networking is proposed for dynamic environments. In the AI-enabled UAV-aided wireless networks (UAWN), multiple UAVs are employed as aerial base stations, which are capable of rapidly adapting to the dynamic environment by collecting information about the users' position and tele-traffic demands, learning from the environment and acting upon the feedback received from the users. Moreover, AI enables the interaction amongst a swarm of UAVs for cooperative optimization of the system. As a benefit of the AI framework, several challenges of conventional UAWN may be circumvented, leading to enhanced network performance, improved reliability and agile adaptivity. As a further benefit, dynamic trajectory design and resource allocation are demonstrated. Finally, potential research challenges and opportunities are discussed.

Otter.ai, an A.I.-powered transcription app and note-takers' best friend, has received a strategic investment from Japan's leading mobile operator and new Otter partner, NTT DOCOMO Inc. The two companies are teaming up to support Otter's expansion into the Japanese market where DOCOMO will be integrating Otter with its own A.I.-based translation service subsidiary, Mirai Translation, in order to provide accurate English transcripts which are then translated into Japanese. The investment was made by DOCOMO's wholly-owned subsidiary, NTT DOCOMO Ventures, Inc., but the size was undisclosed. However, the new round was $10 million in total, we're told. To date, Otter has raised $23 million in funding from NTT DOCOMO Ventures, Fusion Fund, GGV Capital, DFJ Dragon Fund, Duke University Innovation Fund, Harris Barton Asset Management, Slow Ventures, Horizons Ventures, and others.

Abstract--Mobile edge learning is an emerging technique that enables distributed edge devices to collaborate in trainin g shared machine learning (ML) models by exploiting their local data samples and communication/computation resources. T o deal with the stragglers dilemma issue faced in this technique, this p aper proposes a new device-to-device (D2D)-enabled data sharin g approach, in which different edge devices share their data samples among each other over D2D communication links, in order to properly adjust their computation loads for increa sing the training speed. Under this setup, we optimize the radio resource allocation for both D2D-enabled data sharing and distributed training, with the objective of minimizing the total training delay under fixed numbers of local and global iterat ions (for training). Numerical results show that the proposed D2 D-enabled data sharing design significantly reduces the train ing delay, and also enhances the training accuracy when the data samples are non-independent and identically distributed ( non-IID) among edge devices. Mobile edge learning has recently attracted growing research interests from both academia and industry to enable various new artificial intelligence (AI) applications such as augmented reality (AR), industrial automation, and autonomous driving [1].

There's plenty in the way of competition in the audio transcription market, which is estimated to be worth $31.82 billion by 2025. But AISense -- the startup behind speech-to-text service Otter -- has managed to carve out a niche for itself in the four years since its founding. Today the company confirmed to VentureBeat that it has raised $10 million in a round led by strategic backer NTT Docomo's Docomo Ventures, with participation from Fusion Fund, GGV, Dragon Dragon Fund, Duke University Innovation Fund, Harris Barton Asset Management, Slow Ventures, and others. This brings AISense's total venture capital raised to $23 million, following previous rounds totaling $13 million. As part of Docomo's investment, the Tokyo-based mobile phone operator says it's piloting Otter in Berlitz's English language classes in Japan.

TOKYO (Reuters) - SoftBank-backed cloud robotics and artificial intelligence startup CloudMinds is slashing its global workforce as it burns through cash after repeated attempts to list on the stock market, people familiar with the matter said. Headed by former China Mobile 0941.HK research whiz Bill Huang, money-losing CloudMinds is the latest SoftBank 9984.T portfolio company to lay off workers. The job cuts include in China, two sources said, where the bulk of the company's workforce is based and where it generates most of its revenues. All the sources declined to be identified because the information is not public. CloudMinds did not respond to requests for comment and a SoftBank representative declined to comment.

Otter.ai to Bring AI-Powered Meeting Note Collaboration Service to Japan in partnership with NTT DOCOMO Partnership includes Investment and Customer Trials of Otter's Real-Time Transcription Los Altos, CA, January 23, 2020 –Otter.ai DOCOMO made a strategic investment in Otter through its wholly-owned subsidiary NTT DOCOMO Ventures, Inc. and announced plans for its AI-based translation service subsidiary to integrate Otter's meeting note collaboration into its offering to provide highly accurate English transcripts translated into Japanese. As a part of Otter's customer engagement with DOCOMO the Otter Voice Meeting Notes application is being used on a trial basis in Berlitz Corporation's English language classes in Japan. Students use Otter to transcribe and review the content of lessons, click on sections of text, and initiate voice playback. DOCOMO, Otter.ai and Berlitz are expanding their collaboration in language education to verify Otter's effectiveness in the study of English DOCOMO is featuring Otter during demonstrations at the DOCOMO Open House 2020, taking place in the Tokyo Big Sight exhibition complex January 23 and 24, 2020.

In mid December, LinkedIn revealed that Artificial Intelligence (AI) Specialist was the top emerging job for 2020; this may not be of much surprise but it does beg the question of what AI specialists actually do, and what the job entails. What are the exact skills and knowledge businesses need from AI Specialists? Artificial Intelligence is a huge buzz word, and is discussed regularly in the media, sometimes conjuring images of robot workers and other futuristic scenarios. We want to break through the AI haze and confusion, and drill down on what AI actually is; if these specialists are so in demand this calendar year, what is it that they will actually be delivering for small business? Dynamic Business has previously explored what AI is, and today we are building on that understanding, with an interview from Jeff Olson, who is Head of Applied AI & Analytics for ANZ, Cognizant.

An online resource scheduling framework is proposed for minimizing the sum of weighted task latency for all the mobile users, by optimizing offloading decision, transmission power, and resource allocation in the mobile edge computing (MEC) system. Towards this end, a deep reinforcement learning (DRL) method is proposed to obtain an online resource scheduling policy. Firstly, a related and regularized stacked auto encoder (2r-SAE) with unsupervised learning is proposed to perform data compression and representation for high dimensional channel quality information (CQI) data, which can reduce the state space for DRL. Secondly, we present an adaptive simulated annealing based approach (ASA) as the action search method of DRL, in which an adaptive h-mutation is used to guide the search direction and an adaptive iteration is proposed to enhance the search efficiency during the DRL process. Thirdly, a preserved and prioritized experience replay (2p-ER) is introduced to assist the DRL to train the policy network and find the optimal offloading policy. Numerical results are provided to demonstrate that the proposed algorithm can achieve near-optimal performance while significantly decreasing the computational time compared with existing benchmarks. It also shows that the proposed framework is suitable for resource scheduling problem in large-scale MEC networks, especially in the dynamic environment.

Qualcomm is a company of inventors that unlocked 5G - ushering in an age of rapid acceleration in connectivity and new possibilities that will transform industries, create jobs, and enrich lives. But this is just the beginning. It takes inventive minds with diverse skills, backgrounds, and cultures to transform 5Gs potential into world-changing technologies and products. This is the Invention Age - and this is where you come in. In addition to world-renowned strengths in wireless connectivity solutions, Qualcomm has invested significantly for years in research and development in advanced sensor technology, machine learning(ML) and artificial intelligence(AI) for environment sensing, perception and cognition.

Zinier, a company that uses artificial intelligence (AI) to automate field work, has raised $90 million in a Series C funding round, bringing its total amount raised to $120 million. The startup plays heavily in the telecom sector -- 80% of its existing customers are in the space, including network operators, equipment vendors and suppliers, contractors, and engineers, according to Zinier's co-founder and CEO Arka Dhar. That's also reflected by the firms that returned to invest in this latest round, including Nokia-backed NGP Capital and Qualcomm Ventures. New investor Iconiq Capital led the round with participation from Tiger Global Management, Accel, Founders Fund, and Newfund Capital. "Zinier is going to play a very, very important role there," Dhar said in a phone interview.