Artificial Intelligence tools and applications have skillfully tried to manage the analysis, diagnosis, tracing, and development of the pandemic in ways unthinkable with manpower solely. The greatest dilemma with this pandemic was that no one knew what it was and how it would react during the beginning of the pandemic. To make matters worse, Covid 19 has been rapidly mutating since its start, and researchers around the world aren't still quite prepared to interact with such a delicate mutating variant that has claimed hundreds and thousands of lives and has essentially changed the course of history forever. This is where AI's prowess comes into play. With deep learning and the combination of researchers from all around the world, Artificial Intelligence has helped us combat the pandemic in unimaginable ways. The foremost task of AI was to collect as much data as possible about the Coronavirus.
While global economic and social uncertainties in 2020 caused significant stress, progress in intelligent technologies continued. The digital and intelligent transformation of all industries significantly accelerated, with AI technologies showing great potential in combatting COVID-19 and helping people resume work. Understanding future technology trends may never have been as important as it is today. Baidu Research is releasing our prediction of the 10 technology trends in 2021, hoping that these clear technology signposts will guide us to embrace the new opportunities and embark on new journeys in the age of intelligence. In 2020, COVID-19 drove the integration of AI and emerging technologies like 5G, big data, and IoT.
Abstract: The spread of COVID-19 has coincided with the rise of Graph Neural Networks (GNNs), leading to several studies proposing their use to better forecast the evolution of the pandemic. Many such models also include Long Short TermMemory (LSTM) networks, a common tool for time series forecasting. In this work, we further investigate the integration of these two methods by implementing GNNs within the gates of an LSTM and exploiting spatial information. In addition, we introduce a skip connection which proves critical to jointly capture the spatial and temporal patterns in the data. We validate our daily COVID-19 new cases forecast model on data of 37 European nations for the last 472 days and show superior performance compared to state-of-the-art graph time series models based on mean absolute scaled error (MASE).
It has been estimated that 1.7 million people die from Tuberculosis (TB), and more than 10.4 million new cases are reported every year worldwide. The global'End TB' strategy aims to eliminate the disease by 2030. However, realizing this goal would be challenging if there were to be a gap in treatment adherence to prescribed medication. In the context of TB and HIV coinfection, non-adherence to the medication has been associated with the incidence of drug resistance, prolonged infection, unsuccessful treatments, and death. Africa experiences a severe shortage of healthcare workers, making delivering proper healthcare difficult.
AI Infrastructure involves the use of deep learning machine technology which allows a machine to develop a hierarchical structure. For example, the first layer of an image can be scanned for basic edges. It is then followed by a layer that gathers the edge forming shapes and finally a layer that can recognize the machine parts. After scanning hundreds of such layers to recognize the needed information, the neural network of the machine can then convert the features into an algorithm, which then is used to recognize the image. AI infrastructure allows to consistently manage data.
"Computers are able to see,hear and learn.Welcome to the future." According to the World Economic Forum,more than 65% of students will work in jobs that don't even exist today.We want to help prepare them for that future by getting them excited about what computer science (CS) can take them.With a focus on girls and others who are underrepresented in the field today. Robotics and automation are dramatically reshaping the global economy.From delivering faster customer service to better quality products and efficient operations, robotics and automation provide enormous value for organizations that adopt them at scale. "Robots and automation will take 800 million jobs by 2030."-McKinsey.Using AI, the company hopes to teach the robot to copy human movements automatically, so that it can operate without a pilot. From the initially reported outbreak of coronavirus (COVID-19) in China to the spread of it across the globe, Medtech companies are rolling out robots and drones to help fight it and provide services and care to those quarantined or practicing social distancing. This pandemic has fast-tracked the "testing" of robots and drones in public as officials seek out the most expedient and safe way to grapple with the outbreak and limit contamination and spread of the virus.
COVID-19 is one of the deadliest viruses, which has killed millions of people around the world to this date. The reason for peoples' death is not only linked to its infection but also to peoples' mental states and sentiments triggered by the fear of the virus. People's sentiments, which are predominantly available in the form of posts/tweets on social media, can be interpreted using two kinds of information: syntactical and semantic. Herein, we propose to analyze peoples' sentiment using both kinds of information (syntactical and semantic) on the COVID-19-related twitter dataset available in the Nepali language. For this, we, first, use two widely used text representation methods: TF-IDF and FastText and then combine them to achieve the hybrid features to capture the highly discriminating features. Second, we implement nine widely used machine learning classifiers (Logistic Regression, Support Vector Machine, Naive Bayes, K-Nearest Neighbor, Decision Trees, Random Forest, Extreme Tree classifier, AdaBoost, and Multilayer Perceptron), based on the three feature representation methods: TF-IDF, FastText, and Hybrid. To evaluate our methods, we use a publicly available Nepali-COVID-19 tweets dataset, NepCov19Tweets, which consists of Nepali tweets categorized into three classes (Positive, Negative, and Neutral). The evaluation results on the NepCOV19Tweets show that the hybrid feature extraction method not only outperforms the other two individual feature extraction methods while using nine different machine learning algorithms but also provides excellent performance when compared with the state-of-the-art methods. Natural language processing (NLP) techniques have been developed to assess peoples' sentiments on various topics.
"Coronavirus disease 2019 (COVID-19) is a highly infectious disease caused by severe acute respiratory syndrome coronavirus 2". "The disease first originated in December 2019 from Wuhan, China and since then it has spread globally across the world affecting more than 200 countries. The impact is such that the World Health Organization(WHO) has declared the ongoing pandemic of COVID-19 a Public Health Emergency of International Concern." As of 29th April, there are a total of 31,30,191 cases with 2,17,674 deaths in more than 200 countries across the world. So, in this particular scenario, one primary thing that needs to be done and has already started in the majority of the countries is Manual testing, so that the true situation can be understood and appropriate decisions can be taken.
The COVID-19 outbreak is causing havoc on the world's economy and public health. Until now, more than 27 million confirmed cases have been reported around the world. Because of the rising number of confirmed instances and the challenges posed by COVID-19 mutations, it is critical to classify healthy and infected people as soon as possible in order to control and treat COVID-19. During initial phases of, manual testing technique was used to detect the COVID -19 patients. However, manual testing has several disadvantages, like limited availability of testing kits, high prices, and inefficient blood tests. As a result, Deep Learning can be used to overcome these obstacles in order to provide a more effective and efficient treatment.
This report from the Montreal AI Ethics Institute (MAIEI) covers the most salient progress in research and reporting over the second half of 2021 in the field of AI ethics. Particular emphasis is placed on an "Analysis of the AI Ecosystem", "Privacy", "Bias", "Social Media and Problematic Information", "AI Design and Governance", "Laws and Regulations", "Trends", and other areas covered in the "Outside the Boxes" section. The two AI spotlights feature application pieces on "Constructing and Deconstructing Gender with AI-Generated Art" as well as "Will an Artificial Intellichef be Cooking Your Next Meal at a Michelin Star Restaurant?". Given MAIEI's mission to democratize AI, submissions from external collaborators have featured, such as pieces on the "Challenges of AI Development in Vietnam: Funding, Talent and Ethics" and using "Representation and Imagination for Preventing AI Harms". The report is a comprehensive overview of what the key issues in the field of AI ethics were in 2021, what trends are emergent, what gaps exist, and a peek into what to expect from the field of AI ethics in 2022. It is a resource for researchers and practitioners alike in the field to set their research and development agendas to make contributions to the field of AI ethics.