Dennis Honrud’s family has been farming wheat in eastern Montana for three generations. Unashamedly old school, Honrud sows only half his 6,000 acres, leaving the rest fallow to avoid soil depletion. “There’s not many of us left,” he laments. Like many workers in the global economy, the 68-year-old needs to stay connected, in his case to monitor crop prices and weather updates from his green John Deere tractor. So he asked a telecom provider to put a cell tower in his backyard.
The Honrud property in Glasgow, Mont., is so remote that it wasn’t well covered by any of the big four American carriers–Verizon, AT&T, T-Mobile and Sprint. So Honrud turned to the local provider, Nemont Wireless, to install the tower. Today, cell service is pretty good. When the occasional car accident happens on the stretch of highway next to the Honrud farm, highway patrol officers no longer need to drive a mile to get a signal. Now they can place a call from the scene. If that hasn’t saved a life yet, “at some point in time it will,” Honrud says.
But there’s a problem. Like around a quarter of the smaller “tier 3” carriers catering to rural areas like Glasgow, Nemont uses equipment provided by Huawei, the world’s biggest telecommunications-equipment company. The Chinese firm generated a mind-boggling $107 billion in revenue last year, selling equipment to customers in 170 countries and regions around the world. It also may be the most controversial company in the world. Read More
Daily Archives: May 25, 2019
The AI Roles Some Companies Forget to Fill
AI is almost everywhere in the news today, and the drive to create and implement AI solutions is creating an enormous talent gap. An estimated 80% of companies are already investing in AI and most are facing challenges hiring the capabilities they need to implement a useful AI application or product. It’s clear that there is an intensively competitive market for artificial intelligence and machine learning specialists. Many companies first attempt to hire Ph.D.-level data scientists with expertise in AI algorithms and “feature engineering.” Some analysts have even equated “AI talent” with such researchers.
However, AI talent goes far beyond machine learning Ph.D’s. Equally important and less understood are the set of talent issues emerging around AI product development and engineering. Most firms have not filled these roles, and their AI projects are suffering as a result. Read More
Few-Shot Adversarial Learning of Realistic Neural Talking Head Models
Several recent works have shown how highly realistic human head images can be obtained by training convolutional neural networks to generate them. In order to create a personalized talking head model, these works require training on a large dataset of images of a single person. However, in many practical scenarios, such personalized talking head models need to be learned from a few image views of a person, potentially even a single image. Here, we present a system with such few-shot capability. It performs lengthy meta-learning on a large dataset of videos, and after that is able to frame few- and one-shot learning of neural talking head models of previously unseen people as adversarial training problems with high capacity generators and discriminators. Crucially, the system is able to initialize the parameters of both the generator and the discriminator in a person-specific way, so that training can be based on just a few images and done quickly, despite the need to tune tens of millions of parameters. We show that such an approach is able to learn highly realistic and personalized talking head models of new people and even portrait paintings. Read More
Mona Lisa frown: Machine learning brings old paintings and photos to life
Machine learning researchers have produced a system that can recreate lifelike motion from just a single frame of a person’s face, opening up the possibility of animating not just photos but also paintings. It’s not perfect, but when it works, it is — like much AI work these days — eerie and fascinating.
The model is documented in a paper published by Samsung AI Center, which you can read here on Arxiv. It’s a new method of applying facial landmarks on a source face — any talking head will do — to the facial data of a target face, making the target face do what the source face does. Read More
How single neurons and brain networks support spatial navigation
Spatial navigation is an essential cognitive function, which is frequently impaired in patients suffering from neurological and psychiatric disorders. Research groups worldwide have studied the neuronal basis of spatial navigation, and the activity of both individual nerve cells and large cell assemblies in the brain appear to play a crucial role in the process. However, the relationship between the behaviour of individual cells and the behaviour of large cell networks has for the most part remained unexplored.
Various theories on this topic were put forward by an international team in the journal “Trends in Cognitive Sciences” from 24 May 2019. The review article was jointly authored by Dr. Lukas Kunz from the University Medical Center in Freiburg, Professor Liang Wang from the Chinese Academy of Sciences in Beijing, and Professor Nikolai Axmacher from Ruhr-Universität Bochum, together with colleagues from Columbia University in New York. Read More
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