You've probably heard that we're in the midst of an A.I. revolution.

When U.N. member states unanimously adopted the 2030 Agenda in 2015, the narrative around global development embraced a new paradigm of sustainability and inclusion--of planetary stewardship alongside economic progress, and inclusive distribution of income. This comprehensive agenda--merging social, economic and environmental dimensions of sustainability--is not supported by current modes of data collection and data analysis, so the report of the High-Level Panel on the post-2015 development agenda called for a "data revolution" to empower people through access to information.1 Today, a central development problem is that high-quality, timely, accessible data are absent in most poor countries, where development needs are greatest. In a world of unequal distributions of income and wealth across space, age and class, gender and ethnic pay gaps, and environmental risks, data that provide only national averages conceal more than they reveal. This paper argues that spatial disaggregation and timeliness could permit a process of evidence-based policy making that monitors outcomes and adjusts actions in a feedback loop that can accelerate development through learning. Big data and artificial intelligence are key elements in such a process. Emerging technologies could lead to the next quantum leap in (i) how data is collected; (ii) how data is analyzed; and (iii) how analysis is used for policymaking and the achievement of better results. Big data platforms expand the toolkit for acquiring real-time information at a granular level, while machine learning permits pattern recognition across multiple layers of input. Together, these advances could make data more accessible, scalable, and finely tuned. In turn, the availability of real-time information can shorten the feedback loop between results monitoring, learning, and policy formulation or investment, accelerating the speed and scale at which development actors can implement change.

There have been many cases of misdiagnosis in Kenya. The Kenya Medical Practitioners and Dentists Board blames machines for these cases. However, this is misleading since machines do not interpret the results. This is the role of medical personnel. Ironically, machines powered by Artificial Intelligence (AI) are learning fast and becoming so clever that it will be easier for medics to make more accurate decisions.

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.