Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. A trio of researchers at Osaka University has now found a method for identifying and quantitatively evaluating facial movements on their android robot child head. Named Affetto, the android's first-generation model was reported in a 2011 publication.

Huawei has unveiled its new smart cities digital platform utilising artificial intelligence (AI) and Internet of Things (IoT) capabilities, which it said could be used across smart public safety, environmental protection, transportation, government, education, and agriculture. Huawei's AI Digital Platform connects what it calls the brain, or command centre; the central nervous system, or network; and the peripheral nervous system, made up of sensors across a city. "Just like an operating system, the platform is compatible with different city sensors, creates a city digital twin, and supports diverse city applications," Huawei Enterprise Business Group VP Ma Yue said. The smart cities digital platform combines AI, IoT, big data, a geographic information system, video, cloud, converged communications, and security. "Huawei has also developed a middleware platform to provide services to software application partners. This is designed to help application partners quickly develop upper-layer applications to accelerate transformation and innovation in city management, city services, and industry development," the Chinese networking giant added.

We compare features for dynamic time warping based keyword spotting in an almost zero-resource setting. The objective is to support United Nations (UN) humanitarian relief efforts in parts of Africa with severely under-resourced languages. As supervised resource, we restrict ourselves to an easily-compiled small set of isolated keywords. For feature extraction, we integrate a multilingual bottleneck feature extractor (BNF), trained on well-resourced out-of-domain languages, with a correspondence autoencoder (CAE), trained on extremely sparse in-domain data. We find that, on their own, BNFs and CAE features achieve more than 2% absolute performance improvement over baseline MFCCs. However, by using BNFs as input to the CAE, even better performance is achieved, with an 11% absolute improvement in ROC AUC over MFCCs and twice as many top-10 retrievals. We conclude that integrating BNFs with the CAE allows both large out-of-domain and sparse in-domain resources to be exploited for improved ASR-free keyword spotting.

Given that South African education is in crisis, strategies for improvement and sustainability of high-quality, up-to-date education must be explored. In the migration of education online, inclusion of machine translation for low-resourced local languages becomes necessary. This paper aims to spur the use of current neural machine translation (NMT) techniques for low-resourced local languages. The paper demonstrates state-of-the-art performance on English-to-Setswana translation using the Autshumato dataset. The use of the Transformer architecture beat previous techniques by 5.33 BLEU points. This demonstrates the promise of using current NMT techniques for African languages.

Between 750 and 1000 hieroglyphs were in use in Egypt thousands of years ago, and to this day we still don't know how to interpret all of them. Could artificial intelligence help us crack the code? How dangerous is AI's exponential growth? Is any job immune to automation? DW spoke to technologists and historians to better understand some of the technological and societal upheavals humanity is facing.

ZHUHAI, CHINA – China is unleashing stealth drones and pilotless aircraft fitted with AK-47 rifles onto world markets, racing to catch up to U.S. technology and adding to a fleet that has already seen combat action in the Middle East. Combat drones were among the jet fighters, missiles and other military hardware shown off this past week at Airshow China, the country's biggest aerospace industry exhibition. A delta-winged stealth drone received much attention, highlighting China's growing production of sophisticated unmanned aerial vehicles seeking to compete with the U.S. military's massive fleet. The CH-7 -- a charcoal-gray UAV unveiled at the air show -- is as long as a tennis court and has a 22-meter (72-feet) wingspan. It can fly at more than 800 kph (500 mph) and at an altitude of 13,000 meters (42,650 feet).

The traditional hiring process is failing candidates, companies and recruiters. It revolves around human interpretation of complex data that is too susceptible to prejudices and mental shortcuts. A hiring process assisted by machine learning could eliminate systemic biases that put social status over skill. However, in recent news, algorithms have failed at that task. A resume screening algorithm developed at Amazon became biased against female applicants.

Perceptual capabilities of artificial systems have come a long way since the advent of deep learning. These methods have proven to be effective, however they are not as efficient as their biological counterparts. Visual attention is a set of mechanisms that are employed in biological visual systems to ease computational load by only processing pertinent parts of the stimuli. This paper addresses the implementation of top-down, feature-based attention in an artificial neural network by use of externally controlled neuron gating. Our results showed a 5% increase in classification accuracy on the CIFAR-10 dataset versus a non-gated version, while adding very few parameters. Our gated model also produces more reasonable errors in predictions by drastically reducing prediction of classes that belong to a different category to the true class. Keywords: feature-based attention, neural networks, top-down attention 1. Introduction Artificially intelligent agents are often designed with bottom-up processing mind. Agents perceive the world through sensors, make decisions based on the sensory information and then perform an action based on the decisions.

Based on 46 in-depth interviews with scientists, engineers, and CEOs, this document presents a list of concrete machine research problems, progress on which would directly benefit tech ventures in East Africa. The goal of this work is to give machine learning researchers a fuller picture of where and how their efforts as scientists can be useful. The goal is thus not to highlight research problems that are unique to East Africa -- indeed many of the problems listed below are of general interest in machine learning. The problems on the list are united solely by the fact that technology practitioners and organizations in East Africa reported a pressing need for their solution. The author is aware that listing machine learning problems without also providing data for them is not a recipe for getting those problems solved. If the reader is interested in any of the problems below, please get in touch.

These intelligent, complex and emotional beasts have come to symbolize wildlife conservation efforts around the globe. Across the African continent, they are under threat from encroaching human settlements and continued poaching -- in the summer of 2018, an aerial survey in Botswana found 87 elephants had been killed and their tusks cut off for their ivory, the biggest slaughter in recent years. But elephants are part of a complex ecosystem and scientists and conservationists know protecting them isn't just crucial to the species itself, but for other African animals, birds and plants. Elephants are a keystone species -- their presence and activities play a crucial role in maintaining the health of the savannah and forests they inhabit. For example, they use their tusks to dig for water, providing this scarce resource for other species as well.