Next-generation network analytics driven by artificial intelligence and machine learning promise to revolutionize conventional infrastructure management models, simplifying operations, reducing costs, and providing fresh insights. Yet, as with many new technologies, AI-fueled analytics can only deliver its promised benefits if applied correctly to the proper problems. AI can be trained to pinpoint network failures and other shortcomings and bottlenecks, sometimes even before they happen. "It can diagnose the root cause of poor quality network streams to find if the problem is in the service provider's network, the backbone network or your ISP's network," said Shervin Shirmohammadi, a professor in the School of Electrical Engineering and Computer Science at the University of Ottawa, and an IEEE Fellow. "It can also solve network congestion (issues), provide bandwidth and delay estimation for better video or gaming experience, provide fair bandwidth allocation to users or within cloud data centers, fix insufficient network utilization and, in general, achieve a higher network performance and a happier customer," he added.

Fresh off winning a major 5G hardware supply contract with Verizon, Ericsson announced today that it is increasing its investments in the United States to speed deployments of 5G, AI, and automation technologies in North America. Under the plan, Ericsson says that it will hire more U.S. personnel for its R&D and manufacturing initiatives, as well as shortening timelines between new products' introduction and delivery to customers. "The United States is our largest market," explained Ericsson CEO Börje Ekholm, "accounting for a quarter of Ericsson's business over the last seven years. To serve the demand of these fast-moving service providers, we are strengthening our investment in the U.S. to be even closer to our customers and meet their accelerated 5G deployment plans." On the R&D side, Ericsson says it will expand the work of a recently opened 5G chip design center in Austin, Texas, as well as opening a new baseband software development center this year, collectively supporting nearly 300 employees.

In The Optical Society's journal for high-impact research, Optica, the researchers report teaching a type of machine learning algorithm known as a deep neural network to recognize images of numbers from the pattern of speckles they create when transmitted to the far end of a fiber. The work could improve endoscopic imaging for medical diagnosis, boost the amount of information carried over fiber-optic telecommunication networks, or increase the optical power delivered by fibers. "We use modern deep neural network architectures to retrieve the input images from the scrambled output of the fiber," said Demetri Psaltis, Swiss Federal Institute of Technology, Lausanne, who led the research in collaboration with colleague Christophe Moser. "We demonstrate that this is possible for fibers up to 1 kilometer long" he added, calling the work "an important milestone." Optical fibers transmit information with light.

Through innovative use of a neural network that mimics image processing by the human brain, a research team reports accurate reconstruction of images transmitted over optical fibers for distances of up to a kilometer. In the Optical Society's journal for high-impact research, Optica, the researchers report teaching a type of machine learning algorithm known as a deep neural network to recognize images of numbers from the pattern of speckles they create when transmitted to the far end of a fiber. The work could improve endoscopic imaging for medical diagnosis, boost the amount of information carried over fiber-optic telecommunication networks, or increase the optical power delivered by fibers. "We use modern deep neural network architectures to retrieve the input images from the scrambled output of the fiber," said Demetri Psaltis, Swiss Federal Institute of Technology, Lausanne, who led the research in collaboration with colleague Christophe Moser. "We demonstrate that this is possible for fibers up to 1 kilometer long" he added, calling the work "an important milestone."

Our guest post is by Brandon Van Volkenburgh, a leading expert in progressive technologies including cloud computing, blockchain, and machine learning. Brandon has played an integral role in the creation of innovative CrowdReason products such as TotalPropertyTax and MetaTasker. By now, I've attended multitudes of tech conferences to learn about machine learning. And amidst the excitement generated by presentations, Q & A sessions, and keynote speakers, there's usually an undercurrent of perplexity--where was the machine learning example that would show us how to get started using this technology? Most conference attendees (and businesspeople in general) agree that machine learning has great potential, but they also have practical concerns: how can I actually use it in my day-to-day business?

While Qualcomm's recently announced Snapdragon 700 chip seemed like its answer for AI in lower cost phones, the company hasn't forgotten about its mid-tier series. The new Snapdragon 670 also centers on speedier AI performance when it comes to computer vision, as it's around 1.8-times faster than the previous generation Snapdragon 660. Notably, the Hexagon 685 DSP delivering all of that speed first appeared on the high-end Snapdragon 845 last December. The Snapdragon 670's Adreno 615 GPU is 25 percent faster than before, making it even better suited for games (which will also see a benefit from the faster AI performance). And the new Kryo 360 CPU is 15 percent faster than the last chip.

Artificial intelligence (AI) is going to make everything smarter, from the way we drive to how diseases are diagnosed. In the latest iteration of 101 Seconds, Qualcomm Technologies' head of Artificial Intelligence and Machine Learning Product Management, Gary Brotman, explains the difference between AI, machine learning, and deep learning, and the different ways on-device AI and AI-integrated machines will make our lives easier and more exciting.

All it takes is one misinterpreted text to land you in a heap of trouble with a friend, significant other, or colleague. Even serial texters aren't immune -- studies show that most recipients fail to tell the difference between sarcasm and seriousness about 44 percent of the time. That's why Es Lee, a Harvard graduate with a degree in computer science, founded Mei, a mobile messaging startup that leverages machine learning to suss out the subtext of conversations. "One of the difficulties of maintaining relationships through text is that it's [possible] to come across as crass or rude -- even when that was never the intention," Lee told VentureBeat in a phone interview. "Emotion is lost in text messages. Mei, which launched in beta earlier this year, is built on the back of "millions" of messages sourced from the app's more than 100,000 users, data from two universities, and the dev team's own exchanges. Lee claims it's one of the largest datasets of its kind. Using natural language processing and sophisticated algorithms that take into account response time, terseness, word choice, and other factors, Mei builds a psychological profile of your texting partners. It's more nuanced than you might expect; Lee said that it's able to determine the gender and age of a person from nothing more than the types of emoji they use. Add messages to the picture, and Mei can tease out the type of relationship between two people -- and the strength of that relationship. "When you're a 25-year-old woman texting a 40-year-old man, you might think that from the one-word messages he's sending, he's not into you," Lee said. "But our data shows otherwise." In practice, Mei calculates a compatibility percentage, scoring people across five key traits -- openness, emotional control, extraversion, agreeableness, and conscientiousness -- and breaking each into subscores (e.g., "self-focused," "contrary," "respectful"). It also highlights the top characteristics they share in common, like "proudness" and "seriousness." It's much more personalized than the feedback most relationship apps are able to provide, Lee said. AI chatbots like NTT Resonant's Oshi-el are trained on common questions and answers, but Mei promises to take each of your interactions into account. "Our idea is to use aggregated data to improve relationships with people.

India plans to roll out state-of-the-art 5G telecom services in the next four years, a senior official said, as the nation rushes to catch up with its Asian peers. "We are not there yet," Telecom Secretary Aruna Sundararajan said in an interview in New Delhi, adding that the complete rollout of 5G will be done by 2022. "5G won't be driven by supply, it'll be driven by demand and the rest of industry needs to wake up to this." The South Asian nation, traditionally a laggard in embracing the latest technology in telecommunications, will follow South Korea, Japan and China, countries where 5G service will be offered within the next two years. The high-speed and low-latency service will help Prime Minister Narendra Modi's Digital India plan, which seeks to broaden Internet access.

Researchers at the Advanced Telecommunications Research Institute International in Japan have created a brain-machine interface for the manipulation of a robotic arm. Researchers at the Advanced Telecommunications Research Institute International in Japan have created a brain-machine interface (BMI) for manipulating a robotic arm without requiring the use of hands. Their system enables the user to control three limbs at once, including two natural arms and the robotic arm. The researchers demonstrated the system by having volunteers balance a ball on a flat surface while the system recorded their brain wave activity; afterward, when a volunteer would think about balancing the ball, the system would recognize the brain wave pattern and move the robot arm to move the ball in a similar way. The researchers found some volunteers were more successful than others in having the robot arm balance the ball, which the researchers said probably was due to the volunteers, not the system.