In a recent study published in the journal Nature, researchers developed and evaluated an approach that used machine-learning algorithms to analyze mobile phone and satellite data to estimate poverty. They aimed to optimize the'Novissi' flagship emergency social assistance program in Togo, West Africa, providing subsistence cash relief to those most affected by COVID-19. Study: Machine learning and phone data can improve targeting of humanitarian aid. The coronavirus disease 2019 (COVID-19) pandemic has had devastating consequences in low- and lower-middle-income countries (LMICs). The living standards of the most economically vulnerable individuals have further worsened with a transition toward extreme poverty.

USM Business Systems has posted a manifold of articles on the emergence and benefits of Artificial Intelligence (AI) technology. From the travel, healthcare, and e-commerce to banking, finance, and entertainment sectors, AI technologies have grabbed the highest priority. Businesses across the globe have a strong belief that revolutionizing AI technology assists them in automating services, reaching the audience, delivering better customer experiences, and generating a strong sales pipeline. In this article, we would like to give you a detailed guide on how AI technology is adopting by industries and what benefits the brands are enjoying by implementing AI in mobile apps. Artificial Intelligence technology is increasingly adopting for mobile apps development.

The deep learning revolution incited by the 2012 Alexnet paper has been transformative for the field of computer vision. Many problems which were severely limited using classical solutions are now seeing unprecedented success. The rapid proliferation of deep learning methods has led to a sharp increase in their use in consumer and embedded applications. One consequence of consumer and embedded applications is lossy multimedia compression which is required to engineer the efficient storage and transmission of data in these real-world scenarios. As such, there has been increased interest in a deep learning solution for multimedia compression which would allow for higher compression ratios and increased visual quality. The deep learning approach to multimedia compression, so called Learned Multimedia Compression, involves computing a compressed representation of an image or video using a deep network for the encoder and the decoder. While these techniques have enjoyed impressive academic success, their industry adoption has been essentially non-existent. Classical compression techniques like JPEG and MPEG are too entrenched in modern computing to be easily replaced. This dissertation takes an orthogonal approach and leverages deep learning to improve the compression fidelity of these classical algorithms. This allows the incredible advances in deep learning to be used for multimedia compression without threatening the ubiquity of the classical methods. The key insight of this work is that methods which are motivated by first principles, i.e., the underlying engineering decisions that were made when the compression algorithms were developed, are more effective than general methods. By encoding prior knowledge into the design of the algorithm, the flexibility, performance, and/or accuracy are improved at the cost of generality...

To help the company and its customers gain value from this data deluge, the Dell IT organization manages a massive data lake and a world-class set of tools for data analytics, machine learning, deep learning and artificial intelligence. At the heart of this data environment is a Greenplum database, a massively parallel data platform for structured data analytics, machine learning and AI. In a typical use case, this raw data gets parsed in Hadoop into a structured format, and then that structured data gets pumped into the Greenplum database, so business and IT users can consume it in analytics applications. The data is used by Dell Technologies employees and customers in the Americas, Europe, the Middle East, Asia and other geographic regions, according to Darryl Smith, chief data platform architect and distinguished engineer at Dell Technologies. For the full story, see the Dell Technologies case study "Analytics and AI in a massive data lake."

A new wave of change in how consumers interact with services is on the horizon, driven by the growing demand for rapid responses from customers. According to Okwechime, AI-powered Chatbots leverage Natural Language Processing to understand conversations and their contexts to such an advanced level that appropriate responses can be generated. He explained that recent advances have gone beyond text-based chats, with companies like Apple (Siri), Google, and Amazon (Alexa), introducing voice-controlled virtual assistants, able to answer an infinite number of questions proficiently in natural language dialogue. "In a market like Africa where literacy rate falls heavily behind the world's average, voice-controlled products and services will revolutionise how a consumer interacts with technology platforms, reducing entry barriers and improving technology adoption," he said.

Why, I wondered, was the enthusiasm so high at this week's Techonomy Climate conference in Mountain View? So I asked a smart friend why climate action suddenly commands so much passion. "The pandemic helped people realize a disaster can strike everyone on the planet all at once," they answered. "Almost none of us really thought it was possible before." It was as good an explanation as any.

John Quackenbush was frustrated with Google. It was January 2020, and a team led by researchers from Google Health had just published a study in Nature about an artificial intelligence (AI) system they had developed to analyze mammograms for signs of breast cancer. The system didn't just work, according to the study, it worked exceptionally well. When the team fed it two large sets of images to analyze--one from the UK and one from the U.S.--it reduced false positives by 1.2 and 5.7 percent and false negatives by 2.7 and 9.4 percent compared with the original determinations made by medical professionals. In a separate test that pitted the AI system against six board-certified radiologists in analyzing nearly 500 mammograms, the algorithm outperformed each of the specialists. The authors concluded that the system was "capable of surpassing human experts in breast cancer prediction" and ready for clinical trials. An avalanche of buzzy headlines soon followed. "Google AI system can beat doctors at detecting breast cancer," a CNN story declared.

We are trying to use the data that is recorded on the wind turbines to predict failures," Kalyan Veeramachaneni, principal research scientist in the Laboratory for Information and Decision Systems of the Massachusetts Institute of Technology, told DW. Ewald Hesse, CEO of Berlin-based Grid Singularity, says several countries in Africa would leapfrog the development phase of European energy systems, similar to what happened to landline phones. "In developing countries, there is no stringent regulation in the energy sector, and we don't need to convince the government of allowing a new approach to energy production and consumption. Still, local communities would benefit from one PV system in the surrounding area, which, combined with sensors to measure energy consumption, would create a localized market. "Whatever comes out in the energy field in developing countries will be by far smarter and more practical than what we have in Germany," said Hesse, adding that several companies contributed to unlocking potential markets and significant investments in developing countries.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - August 3. Join AI and data leaders for insightful talks and exciting networking opportunities. By this point, it's become reflexive: When searching for something on Google, Wikipedia is the de facto go-to first page. The website is consistently among the top 10 most-visited websites in the world. Yet, not all changemakers and historical figures are equally represented on the dominant web encyclopedia. Just 20% of Wikipedia biographies are about women.

Using algorithms and artificial intelligence, a research team led by Universidad Complutense de Madrid (UCM) has designed a tool which, in its initial trials, proved capable of predicting those areas with best access to potable groundwater in Africa, with a success rate of close to 90%. In specific terms, the papers published in Hydrology and Earth System Science and Geocarto International describe the hydrogeological mapping performed by the MLMapper software in the regions of Bamako and Koulikoro (Mali) and the region of Ouaddaï (Chad), respectively. "Ensure access to water and sanitation for all" is Sustainable Development Goal 6. In sub-Saharan Africa, groundwater plays a fundamental role in the supply of drinking water, but the percentage of wells that strike water is very often lower than 30%. "This is mainly because of a lack of hydrogeological knowledge, with the practical consequence that millions of euros of humanitarian aid are lost in fruitless drilling operations", underlines Víctor Gómez-Escalonilla Canales, a researcher at UCM's Department of Geodynamics, Stratigraphy and Palaeontology.