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How to reduce the carbon footprint of advanced AI models - ITU Hub

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As artificial intelligence (AI) steadily grows, so do concerns about its environmental footprint. Today's emerging natural language processing (NLP) models, such as GPT-3 can consume as much energy as five cars, according to a 2019 study. To reduce their environmental and climate impact, researchers in the United Arab Emirates are proposing a new development approach for these models that takes energy consumption into account at every stage, aiming to boost energy efficiency wherever possible. Last April, Abu Dhabi's Technology Innovation Institute (TII) launched NOOR, the largest Arabic-language NLP model to date. NOOR – Arabic for "light" – is trained on 10 billion parameters including books, poetry, news, and technical information, reinforcing the model's broad applicability, according to its creators.


Full Stack Developer (Angular / Node JS) at AIVA Technologies

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Full Stack Developer (Angular / Node JS) application formAIVA is an Artificial Intelligence that composes music in the matter of seconds. We serve customers worldwide who are interested in using the power of AI to supercharge their creative process. More information can be found on our website.As a Full Stack Developer, you will play a central role in developing, improving and maintaining AIVA's User Interface, as well as building highly scalable RESTful APIs for our enterprise customers. To do so, you will be using NodeJS, Angular 8 and MongoDB, and will interface with our internal Audio and AI Composition APIs.What we are looking forWe are looking for a candidate with experience deploying and managing large-scale cloud services built on the same or similar technology stack. More specifically, we are looking for the following:Essential: At least 3 years of experience as a full stack developer, using Node, MongoDB and Angular (preferably, version 2 or above).


4 AI Trends That Will Shape Up India's Tech Landscape

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We're seeing life-changing advancements in various aspects of our professional and personal lives as AI becomes more integrated into our daily lives From using voice assistants like Alexa and Siri to unlocking our mobile phones using face recognition, we're using the technology more frequently than we think Technology has progressed to model and algorithm-based machine learning, with an emphasis on perception, reasoning, and generalisation. Artificial intelligence (AI) has benefited a variety of industries in recent years and will continue to do so in the future. AI has fuelled the development of numerous advancements, including the Internet of Things (IoT), robotics, analytics, and voice assistants! We're seeing life-changing advancements in various aspects of our professional and personal lives as AI becomes more integrated into our daily lives. From using voice assistants like Alexa and Siri to unlocking our mobile phones using face recognition, we're using the technology more frequently than we think. We see AI's capabilities every day, whether it's on social media when we see a personalised feed, running a Google search about the latest movie we watched and finding the best match for our search even before we finish typing, and much more.


Can Artificial Intelligence Enhance Customer Support?

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With customer service taking center-stage, businesses are keen on implementing new ways to ensure that their customers receive impeccable service. And artificial intelligence is fast gaining momentum in helping businesses ace their customer support. By transforming customer service interactions, AI-powered digital solutions are prepared to improve every aspect of your business including online customer experience, loyalty, brand reputation, preventive assistance and even generation of revenue streams. This blog delves into the subject a little more to convey how AI-powered customer service can possibly help customer support agents online. According to a recent Zendesk study, as much as 42% of B2C customers showed more interest in purchasing after experiencing good customer service.


Inclusion, inequality, and the Fourth Industrial Revolution (4IR) in Africa

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Adoption of Fourth-Industrial-Revolution (4IR) technologies in sub-Saharan Africa could bring not only substantial economic growth and welfare benefits, but also social and economic disruption, including widening inequality if countervailing policies are not adopted, as discussed in our recent report. With a high share of the labor force working informally--a trend expected to continue for several decades--Africa's education and industrial policies need to strike a balance between encouraging private investment needed to create new formal jobs using advanced technology and ensuring that all new labor force entrants have the basic skills and infrastructure to make an adequate living. Much has been written about the current and potential disruptive effects in advanced economies, of the suite of new technologies called the Fourth Industrial Revolution (4IR)--a group of technologies that fuse digital, biological, and physical innovation in applications such as advanced robotics using artificial intelligence, CRISPR digital gene editing, and the networks of sensors and computers called the Internet of Things. Studies estimated that globally in the manufacturing sector alone, 4IR technologies could create 133 million jobs by the end of 2022, but displace 75 million jobs, leading to a net gain of 58 million jobs. Researchers have demonstrated that in the U.S., the skill-bias of technological change in the production sphere disproportionately affected routine and middle-skilled occupations, creating an asymmetry of opportunities, earnings, and income between lower and highly educated workers, and exacerbating inequality trends.


Artificial Intelligence and the fisheries sector

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Firstly, seafood in all of the supply chain is analysed: 1) AI shows promising uses for traceability and seafood product integrity; 2) limited data generation and collection is the main barrier; 3) the processing industry is starting to use AI systems in automation processes; 4) AI proofs-of-concept have been developed into the logistics; and, 5) ML has been used to infer consumer behaviour and economic growth forecasting. The second topic analysed is the use of AI for more selective fishing techniques: 1) there is a mismatch between fisheries selectivity improvement management goals and industry's tools to comply with regulations; 2) species selectivity can be further improved with AI; and, 3) AI systems aimed at automated species forecasting and detection, identification and sizing of catches could allow improving fishing decisions and enable quota tracking. The third discussion topic assesses the use of AI as a driving force for young people to seek jobs in fisheries: 1) AI, similarly to digitalisation, is likely to create new skilled jobs while decreasing the need for low skilled ones in the fisheries sector; 2) the marine transport sector has developed AI systems focused on ship failure prediction and anomaly detection that could be applied in fishing vessels; and, 3) a more digitalised and AI-based fisheries sector might attract new young talent, but will be competing against other industries currently offering higher incentives. Good practices in fisheries that could be useful for future good practices within fisheries sector when developing or using AI are also analysed. Best practices guidelines for different fishery facets are commonly used by management organisations to increase their sustainability and AI technology should follow this example.


Top Neural Network Architectures For Machine Learning Researchers

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The neural networks discussed are specifically referred to as artificial neural networks. As the name implies, they are based on what is known about the structure and operation of the human brain. A neural network is a computing system composed of several crucial yet intricately linked parts, sometimes called "neurons," stacked in layers and processing data using dynamic state reactions to outside inputs. In this structure, designs are communicated to one or more hidden layers present in the network by the input layer, which in this structure has one neuron for each component present in the input data. These layers are only referred to as "hidden" because they do not make up the input or output layer.



Lizard in your luggage? We're using artificial intelligence to detect wildlife trafficking

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Blue-tongue lizards and sulfur-crested cockatoos are among the native animals frequently smuggled overseas. While the number of live animals seized by the Australian Government has tripled since 2017, the full scale of the problem eludes us as authorities don't often know where and how wildlife is trafficked. Now, we can add a new technology to Australia's arsenal against this cruel and inhumane industry. Our research, published in Frontiers in Conservation Science today, shows the potential for new technology to detect illegal wildlife in luggage or mail. This technology uses artificial intelligence to recognize the shapes of animals when scanned at international frontlines such as airports and mail centers.


Artificial Intelligence is Indian Navy's new strategic frontline

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In modern geo-politics the role of Indian Navy is going to be more challenging and its active participation could decide the place of India in global power play. The seminar "Swavlamban" chaired by the PM Modi on SPRINT Challenges on July 18th 2022, is showcasing the seriousness of New Delhi towards the strengthening the Indian Navy through the modern indigenous technologies. The presence of Chinses third generation research and survey ship "Yuan Wang 5" in Hambantota, Sri Lanka, is sufficient to explain that the Indo-Pacific is going to be future coliseum of geo-politics. It is provoking India to adopt modern cutting-edge naval technologies to protect the country's interest and control the foreign powers. Technology is always an important agent, which decides or redefines the war parameters with some distinctive outputs.