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Qualcomm beefs up artificial intelligence team with purchase of Twenty Billion Neurons

#artificialintelligence

Qualcomm said Monday that it recently acquired the assets of Twenty Billion Neurons, a Microsoft-backed artificial intelligence/computer vision startup that develops avatars that can see and interact with people in a human-like way. The San Diego mobile technology company declined to say how much it paid for TwentyBN, which has locations in Berlin and Toronto. But it is likely a relatively small deal. Qualcomm said the company has under 20 employees. It raised about $10 million in venture capital from M12 -- Microsoft's venture capital fund-- and others since it was founded in 2015 by Chief Executive Roland Memisevic. Memisevic was the co-head of MILA, a well-respected AI research institute in Montreal.


Qualcomm's Vision: The Future Of ... AI

#artificialintelligence

Company acquires assets from Twenty Billion Neurons GmbH to bolster its AI Team. Qualcomm Technologies (QTI) is running a series of webinars titled "The Future of...", and the most recent edition is on AI. In this lively session, I hosted a conversation with Ziad Ashgar, QTI VP of Product Management, Alex Katouzian, QTI SVP and GM Mobile Compute and Infrastructure, and Clément Delangue, Co-Founder and CEO of the open source AI model company, Hugging Face, Inc. I've also penned a short Research Note on the company's AI Strategy, which can be found here on Cambrian-AI, where we outline some impressive AI use cases. Qualcomm believes AI is evolving exponentially thanks to billions of smart mobile devices, connected by 5G to the cloud, fueled by a vibrant ecosystem of application developers armed with open-source AI models.


When the Earth is gone, at least the internet will still be working – TechCrunch

#artificialintelligence

The internet is now our nervous system. We are constantly streaming and buying and watching and liking, our brains locked into the global information matrix as one universal and coruscating emanation of thought and emotion. What happens when the machine stops though? It's a question that E.M. Forster was intensely focused on more than a century ago in a short story called, rightly enough, "The Machine Stops," about a human civilization connected entirely through machines that one day just turn off. Those fears of downtime are not just science fiction anymore.


Qualcomm launches Snapdragon 888 Plus, 5G accelerator card, new small cell platform

ZDNet

Qualcomm is looking to boost mmWave 5G adoption across smartphones, infrastructure as well as industrial applications. Building out the infrastructure to support 5G mmWave is going to be critical for everything from industry 4.0 applications to smart cities to keeping unlimited data plans, said Ignacio Contreras, senior director of product marketing at Qualcomm. "As more users go back to normal you'll see more of the difference in what mmWave provides at places like train station, coffee shops, stadiums and trade shows," he said. "Networks need to deliver more capacity to keep unlimited plans affordable." Qualcomm launched the Snapdragon 888 Plus 5G platform for premium tier smartphones rolling out in the second half.


Verizon seems to be making its own Alexa-based smart display

Engadget

It looks like Verizon (Engadget's parent company) is working on an Alexa-powered smart display. However, rather than saying "Alexa" to activate the voice assistant, it appears anyone who buys the device would need to say "Hey, Verizon" instead. Amazon's Alexa Custom Assistant program lets companies build a version of Alexa with custom wake words and device-specific skills. The smart display emerged in Federal Communications Commission filings that were first spotted by Protocol. According to the documents, the Verizon Smart Display has an eight-inch, 1280 800 display, 4GB of RAM and 16GB of storage. The device supports calls, messaging, Alexa announcements and Verizon's BlueJeans video conferencing tool, per the user manual.


AI and ML for Open RAN and 5G

#artificialintelligence

Fast, reliable, and low-latency data services are essential deliverables from telecom operators today. Realizing them is pushing operators to enhance infrastructure, expand network capacity and mitigate service degradation. Unlike other industries, though, telecom networks are vast monoliths comprising fiber optic cables, proprietary components, and legacy hardware. Because of this, there is less enhancing--and more shoring up the creaking infrastructure. Radio access networks (RAN) are the backbone of the telecommunications industry. However, the industry's propensity to incubate and evolve newer, cost-effective, and energy-efficient technologies has been slow due to monopolization by RAN component manufacturers.


Spectral goodness-of-fit tests for complete and partial network data

arXiv.org Machine Learning

Networks describe the, often complex, relationships between individual actors. In this work, we address the question of how to determine whether a parametric model, such as a stochastic block model or latent space model, fits a dataset well and will extrapolate to similar data. We use recent results in random matrix theory to derive a general goodness-of-fit test for dyadic data. We show that our method, when applied to a specific model of interest, provides an straightforward, computationally fast way of selecting parameters in a number of commonly used network models. For example, we show how to select the dimension of the latent space in latent space models. Unlike other network goodness-of-fit methods, our general approach does not require simulating from a candidate parametric model, which can be cumbersome with large graphs, and eliminates the need to choose a particular set of statistics on the graph for comparison. It also allows us to perform goodness-of-fit tests on partial network data, such as Aggregated Relational Data. We show with simulations that our method performs well in many situations of interest. We analyze several empirically relevant networks and show that our method leads to improved community detection algorithms. R code to implement our method is available on Github.


What is 6G, if anything? A guide to what to expect, from whom, and when

ZDNet

If there is to be a "6G Wireless," its proponents will need to learn some significant lessons from the era of 5G. Already, 5G Wireless as a market strategy is four years old. The R&D divisions of telecommunications firms whose 5G rollouts are well under way, are now looking ahead to whatever the next version of wireless may be. . . So far, what they're seeing may be a bit far out. It's a capital improvement project the size of the entire planet, replacing one wireless architecture created this century with another one that aims to lower energy consumption and maintenance costs. "6G must deliver an outcome that is aligned with real needs," remarked David Lister, Head of 6G Research and Development Technology at Europe's Vodafone Group, "and deliver outcomes that are sustainable and commercially driven." Lister was speaking at an annual conference called the 6G Symposium. Yes, there is already an annual 6G Symposium. Back in 1998, the leading stakeholders in global telecommunications formed the 3GPP consortium, to officially designate which technologies belong to a "G" and which don't.


Active Learning for Network Traffic Classification: A Technical Survey

arXiv.org Artificial Intelligence

Network Traffic Classification (NTC) has become an important component in a wide variety of network management operations, e.g., Quality of Service (QoS) provisioning and security purposes. Machine Learning (ML) algorithms as a common approach for NTC methods can achieve reasonable accuracy and handle encrypted traffic. However, ML-based NTC techniques suffer from the shortage of labeled traffic data which is the case in many real-world applications. This study investigates the applicability of an active form of ML, called Active Learning (AL), which reduces the need for a high number of labeled examples by actively choosing the instances that should be labeled. The study first provides an overview of NTC and its fundamental challenges along with surveying the literature in the field of using ML techniques in NTC. Then, it introduces the concepts of AL, discusses it in the context of NTC, and review the literature in this field. Further, challenges and open issues in the use of AL for NTC are discussed. Additionally, as a technical survey, some experiments are conducted to show the broad applicability of AL in NTC. The simulation results show that AL can achieve high accuracy with a small amount of data.


Vivaldi adds mail, calendar, RSS and translation tools to its privacy-focused browser

Engadget

Vivaldi has released a major update for its eponymous web browser for privacy-minded power users. Version 4.0 bring with it a translation tool, along with beta versions of Vivaldi Mail, Calendar, and Feed Reader. The update is available now on Windows, Mac and Linux and Android devices. Vivaldi built its translation feature into its browser. The tool is powered by Lingvanex, a Cyprus-based company that makes translator's for a wider range of platforms including voice calls and smartwatches. As part of its focus on privacy, Vivaldi says that all your translation activity will be kept away from third-parties on its servers in Iceland.