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At CAGR 36.2%, Artificial Intelligence Market 2020: Future Challenges And Industry Growth Outlook 2025

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Artificial Intelligence (AI) is the study of "intelligent agents" which can be define as any device that perceives its environment and takes appropriate action that makes the highest probability of achieving its goals. Additionally, it can also be define as a system's ability to interpret external data, learn from gathered data and use those learnings to realize specific goals through adaptation. It is also called as machine intelligence and attributed to the nature of intelligence demonstrated by machines. Some of the features of artificial intelligence are; successfully understanding human language, contending at the highest level in strategic games systems such as chess and go, autonomously operating cars, intelligent routing in content delivery networks and military simulations and others. To solve the problem of learning and perceiving the immediate environment, many approaches have been taken such as statistical methods, computational intelligence, versions of search and mathematical optimization, artificial neural networks, and methods based on statistic, probability and economics.


Machine learning approaches classify clinical malaria outcomes based on haematological parameters

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Background: Malaria is still a major global health burden, with more than 3.2 billion people in 91 countries remaining at risk of the disease. Accurately distinguishing malaria from other diseases, especially uncomplicated malaria (UM) from non-malarial infections (nMI) remains a challenge. Furthermore, the success of rapid diagnostic tests (RDT) is threatened by Pfhrp2/3 deletions and decreased sensitivity at low parasitemia. Analysis of haematological indices can be used to support identification of possible malaria cases for further diagnosis, especially in travelers returning from endemic areas. As a new application for precision medicine, we aimed to evaluate machine learning (ML) approaches that can accurately classify nMI, UM and severe malaria (SM) using haematological parameters.


Protecting Our Future Food Supply with AI and Geospatial Analytics

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Corn, coffee, chocolate, even wine are a few of the foods that stand to be massively disrupted by the effects of climate change, population growth and water scarcity -- if they haven't already. A recent study found the yields of the world's top ten crops have begun to decrease, a drop that is disproportionately affecting food-insecure countries. The situation stands to worsen. Researchers project that the global population will increase by 3 billion in 2050. To feed these additional global residents, agricultural production must increase by 50 percent, says Dr. Ranga Raju Vatsavai, an associate professor in computer science at North Carolina State University and the associate director of the Center for Geospatial Analytics.


Microsoft unveils updates across Azure Cognitive Services and Azure Machine Learning

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During its Ignite 2020 conference, which kicked off virtually this morning, Microsoft announced updates to Azure Cognitive Services and Azure Machine Learning aimed at streamlining business processes during the coronavirus pandemic. The company also launched two features in Azure Cognitive Search -- Private Endpoints and Managed Identities -- plus enhancements to Bot Framework Composer and the broader Azure Bot Service. "We're seeing AI touching every business across the planet, and so one of the key focuses we have with Azure Machine Learning is to provide our customers with the tools to really simplify the ability to create new models because we know they're going to need them in every area of their business," Microsoft corporate vice president Eric Boyd told VentureBeat in a phone interview. "This continues to be a key theme for us -- how we will really help our customers, enable more of their developers, and even more of their data analysts to build machine learn models and apply them in all aspects of their business." Private Endpoints in Cognitive Search, which is generally available as of today, allow a client on a virtual network to access data in an index over a private link.


Artificial Intelligence and Robotics in Aerospace and Defense Market Analysis Focusing on Top Players, Growth, trends during Forecast Period 2020-2027 - The Market Records

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The Global Artificial Intelligence and Robotics in Aerospace and Defense Market report studies the market comprehensively and provides an all-encompassing analysis of the key growth factors, Artificial Intelligence and Robotics in Aerospace and Defense market share, and the newest developments. Also, the Artificial Intelligence and Robotics in Aerospace and Defense Industry Market report provides growth rate, market demand and supply, and market potential for each geographical region. The Artificial Intelligence and Robotics in Aerospace and Defense report gives information about the Artificial Intelligence and Robotics in Aerospace and Defense market trend and share, market size analysis by region, and analysis of the global market size. The market study analysis presents an analysis of market share and segments by region and growth rate. Regional breakdown includes an in detail study of the key geological regions to gain a better accepting of the market and provide an accurate analysis.


Neural network for low-memory IoT devices

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A scientist from Russia has developed a new neural network architecture and tested its learning ability on the recognition of handwritten digits. The intelligence of the network was amplified by chaos, and the classification accuracy reached 96.3%. The network can be used in microcontrollers with a small amount of RAM and embedded in such household items as shoes or refrigerators, making them'smart.' The study was published in Electronics. Today, the search for new neural networks that can operate on microcontrollers with a small amount of random access memory (RAM) is of particular importance.


Artificial Intelligence (AI) In Fintech Market Growth by Top Companies, Region, Application, Driver, Trends and Forecasts by 2027 – Crypto Daily

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The Artificial Intelligence (AI) In Fintech Market report predicts promising growth and development during the period 2020-2027. The Artificial Intelligence (AI) In Fintech Market survey report represents vital statistical data represented in an organized format such as graphs, charts, tables, and figures to provide a detailed understanding of the Artificial Intelligence (AI) In Fintech Market in a simple manner. The report covers an in-depth analysis of the Artificial Intelligence (AI) In Fintech market and offers key insights on current and emerging trends, market drivers, and market insights offered by industry experts. The report examines the impact of COVID-19 on market growth. The study provides comprehensive coverage of the impact of the COVID-19 pandemic on the Artificial Intelligence (AI) In Fintech market and its key segments.


Integrating AI Ethics into Higher Education Curricula in Africa – RAIN-Africa

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How is AI Ethics and Responsible AI currently being taught in Computer Science and Engineering Curriculums across Africa? What issues related to this topic are relevant to students and faculty? And what roadblocks or challenges are instructors facing to bring more discussion of AI ethics to classrooms? The goal of this workshop is to foster a discussion on how to effectively integrate AI Ethics into Computer Science/Engineering programs at African Universities. This is an initial step to gather perspectives on the current situation at representative universities in different countries in Africa, and to initiate a discussion on how we can better support each other with lessons learned and share materials/curriculums to further develop AI ethics programs in higher education. After identifying the current state, the interests of students and faculty and the needs of departments in this workshop session, the goal is to continue the series with more in-depth workshops on specific topics.


Facebook AI Wav2Vec 2.0: Automatic Speech Recognition From 10 Minute Sample

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Speech-to-text applications have never been so plentiful, popular or powerful, with researchers' pursuit of ever-better automatic speech recognition (ASR) system performance bearing fruit thanks to huge advances in machine learning technologies and the increasing availability of large speech datasets. Current speech recognition systems require thousands of hours of transcribed speech to reach acceptable performance. However, a lack of transcribed audio data for the less widely spoken of the world's 7,000 languages and dialects makes it difficult to train robust speech recognition systems in this area. To help ASR development for such low-resource languages and dialects, Facebook AI researchers have open-sourced the new wav2vec 2.0 algorithm for self-supervised language learning. The paper Wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations claims to "show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler." A Facebook AI tweet says the new algorithm can enable automatic speech recognition models with just 10 minutes of transcribed speech data.


Introduction to Evolutionary Computing, 2nd edition - Programmer Books

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The overall structure of this new edition is three-tier: Part I presents the basics, Part II is concerned with methodological issues, and Part III discusses advanced topics. In the second edition the authors have reorganized the material to focus on problems, how to represent them, and then how to choose and design algorithms for different representations. They also added a chapter on problems, reflecting the overall book focus on problem-solvers, a chapter on parameter tuning, which they combined with the parameter control and "how-to" chapters into a methodological part, and finally a chapter on evolutionary robotics with an outlook on possible exciting developments in this field. The book is suitable for undergraduate and graduate courses in artificial intelligence and computational intelligence, and for self-study by practitioners and researchers engaged with all aspects of bioinspired design and optimization.