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AUSTRALIA with Australian States and Dependencies
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Artificial Intelligence and Learning Futures : Critical Narratives of Technology and Imagination in Higher Education
Artificial Intelligence and Learning Futures: Critical Narratives of Technology and Imagination in Higher Education explores the implications of artificial intelligence’s adoption in higher education and the challenges to building sustainable instead of dystopic schooling.As AI becomes integral to both pedagogy and profitability in today’s colleges and universities, a critical discourse on these systems and algorithms is urgently needed to push back against their potential to enable surveillance, control, and oppression.This book examines the development, risks, and opportunities inherent to AI in education and curriculum design, the problematic ideological assumptions of intelligence and technology, and the evidence base and ethical imagination required to responsibly implement these learning technologies in a way that ensures quality and sustainability.Leaders, administrators, and faculty as well as technologists and designers will find these provocative and accessible ideas profoundly applicable to their research, decision-making, and concerns.
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Which programming language is used for robotics and artificial intelligence?
Python is commonly used for robotics and artificial intelligence due to its simplicity, readability, and extensive libraries for machine learning and robotics. It is also known for its flexibility and ease of integration with hardware, making it a popular choice for developing robotics and AI applications. Additionally, Python's strong community support and active development make it a suitable language for these rapidly evolving fields.
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What types of dependencies exist?
There are various types of dependencies that exist in project management, including finish-to-start, start-to-start, finish-to-finish, and start-to-finish dependencies. Finish-to-start is the most common type, where one task must finish before the next can start. Start-to-start means two tasks must start at the same time. Finish-to-finish requires two tasks to finish at the same time, and start-to-finish means one task must start before the other can finish. Understanding these dependencies is crucial for effective project planning and scheduling.
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What are functional dependencies in space?
Functional dependencies in space refer to the relationships between different spatial attributes or variables within a dataset. These dependencies describe how one spatial attribute can be determined by another, indicating a cause-and-effect relationship between them. Understanding functional dependencies in space is crucial for spatial analysis and modeling, as it helps in identifying patterns, predicting outcomes, and making informed decisions based on spatial data. By recognizing these dependencies, researchers can better understand the underlying processes driving spatial phenomena.
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What are cell dependencies in LibreOffice Calc?
Cell dependencies in LibreOffice Calc refer to the relationship between cells that affects the calculation of a formula. When a formula in one cell depends on the value of another cell, the second cell is considered a dependency of the first cell. This means that if the value of the dependent cell changes, it will affect the result of the formula in the other cell. Understanding cell dependencies is important for ensuring the accuracy of calculations and for troubleshooting errors in spreadsheets.
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Advances in Robotics and Artificial Intelligence
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Artificial Intelligence And Innovation Management
Artificial Intelligence and Innovation Management contributes to the ongoing debate among innovation scholars and practitioners focusing on the potential impact of Artificial Intelligence (AI) on the ways companies and organizations do business, operate and innovate.It considers AI as a source of innovation both in terms of innovation within the field of AI itself (AI innovation) and in terms of how it enables or disrupts innovation in other fields (AI-driven innovation).The book's content is driven by several important conclusions:It is therefore both necessary and timely to explore the different aspects of the relationship between AI and IM.The contributors to this book include both scholars and practitioners from multiple countries and different types of institutions.They were selected based on their ability to provide a relevant distinctive perspective on the relationship between AI and IM; the degree of their professional engagement with the field; their ability to contribute to the thematic and contextual diversity of the contributions; and their ability to provide actionable insights for both innovation scholars and practitioners.Helena Blackbright (Mälardalen University, Sweden) and Stoyan Tanev (Carleton University, Canada) are chairing the Special Interest Group on AI and IM at the International Society for Professional Innovation Management (https://www.ispim-innovation.com/).
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AI projects with Raspberry Pi : High-performance artificial intelligence for robotics, security, home automation, and vision
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Adopting Artificial Intelligence Tools in Higher Education : Teaching and Learning
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Is machine learning already artificial intelligence?
Machine learning is a subset of artificial intelligence. It involves training a machine to learn from data and make predictions or decisions without being explicitly programmed to do so. Artificial intelligence, on the other hand, encompasses a broader range of technologies and applications that enable machines to perform tasks that typically require human intelligence, such as understanding natural language, recognizing patterns, and solving problems. While machine learning is an important component of artificial intelligence, AI also includes other techniques such as natural language processing, computer vision, and robotics.
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What are superkeys and functional dependencies in databases?
In databases, a superkey is a set of attributes that can uniquely identify a tuple in a relation. It is a combination of one or more attributes that uniquely identifies each tuple in a relation. On the other hand, functional dependencies are constraints that describe the relationship between attributes in a relation. They specify how the values of certain attributes determine the values of other attributes in the same relation. Functional dependencies are used to ensure data integrity and normalization in a database schema.
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What values can x take in functional dependencies?
In functional dependencies, the values of x can take on any value from the domain of the attribute it is associated with. For example, if we have a functional dependency A -> B, where A and B are attributes in a relation, the values of A can be any value from the domain of A. This means that for each unique value of A, there is a unique value of B associated with it.
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What dependencies exist in the transportation of teachers?
The transportation of teachers depends on several factors, including the availability of public transportation or school-provided transportation, the location of the school and the teachers' residences, and the teachers' individual transportation options. Additionally, the transportation of teachers may also depend on the school's budget and resources for providing transportation services. The availability of parking at the school or nearby may also impact teachers' transportation options. Overall, the transportation of teachers is dependent on a combination of logistical, financial, and geographical factors.
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