With the development of urbanization,metropolises and megapolises are emerging.Urbanization is being completed with the driving force of capital;meanwhile,technology has achieved the quest of capital.In this thesis,in...With the development of urbanization,metropolises and megapolises are emerging.Urbanization is being completed with the driving force of capital;meanwhile,technology has achieved the quest of capital.In this thesis,in light of Henri Lefebvre’s ideology of technology rational critique,Chinese urban modernity issues under the perspective of technology rational critique were analyzed,including issues like eroded urban texture,urban mechanization,and urban segregation.The technology rational root of Chinese urban modernity issues was discussed from the turn of technical artifacts from design intention to application intention.In the end,possible methods to solve these Chinese urban modernity issues were illustrated based on Henri Lefebvre’s urban development theory.展开更多
Artificial intelligence, often referred to as AI, is a branch of computer science focused on developing systems that exhibit intelligent behavior. Broadly speaking, AI researchers aim to develop technologies that can ...Artificial intelligence, often referred to as AI, is a branch of computer science focused on developing systems that exhibit intelligent behavior. Broadly speaking, AI researchers aim to develop technologies that can think and act in a way that mimics human cognition and decision-making [1]. The foundations of AI can be traced back to early philosophical inquiries into the nature of intelligence and thinking. However, AI is generally considered to have emerged as a formal field of study in the 1940s and 1950s. Pioneering computer scientists at the time theorized that it might be possible to extend basic computer programming concepts using logic and reasoning to develop machines capable of “thinking” like humans. Over time, the definition and goals of AI have evolved. Some theorists argued for a narrower focus on developing computing systems able to efficiently solve problems, while others aimed for a closer replication of human intelligence. Today, AI encompasses a diverse set of techniques used to enable intelligent behavior in machines. Core disciplines that contribute to modern AI research include computer science, mathematics, statistics, linguistics, psychology and cognitive science, and neuroscience. Significant AI approaches used today involve statistical classification models, machine learning, and natural language processing. Classification methods are widely applicable to problems in various domains like healthcare, such as informing diagnostic or treatment decisions based on patterns in data. Dean and Goldreich, 1998, define ML as an approach through which a computer has to learn a model by itself from the data provided but no specification on the sort of model is provided to the computer. They can then predict values for things that are different from the values used in training the models. NLP looks at two interrelated concerns, the task of training computers to understand human languages and the fact that since natural languages are so complex, they lend themselves very well to serving a number of very useful goals when used by computers.展开更多
文摘With the development of urbanization,metropolises and megapolises are emerging.Urbanization is being completed with the driving force of capital;meanwhile,technology has achieved the quest of capital.In this thesis,in light of Henri Lefebvre’s ideology of technology rational critique,Chinese urban modernity issues under the perspective of technology rational critique were analyzed,including issues like eroded urban texture,urban mechanization,and urban segregation.The technology rational root of Chinese urban modernity issues was discussed from the turn of technical artifacts from design intention to application intention.In the end,possible methods to solve these Chinese urban modernity issues were illustrated based on Henri Lefebvre’s urban development theory.
文摘Artificial intelligence, often referred to as AI, is a branch of computer science focused on developing systems that exhibit intelligent behavior. Broadly speaking, AI researchers aim to develop technologies that can think and act in a way that mimics human cognition and decision-making [1]. The foundations of AI can be traced back to early philosophical inquiries into the nature of intelligence and thinking. However, AI is generally considered to have emerged as a formal field of study in the 1940s and 1950s. Pioneering computer scientists at the time theorized that it might be possible to extend basic computer programming concepts using logic and reasoning to develop machines capable of “thinking” like humans. Over time, the definition and goals of AI have evolved. Some theorists argued for a narrower focus on developing computing systems able to efficiently solve problems, while others aimed for a closer replication of human intelligence. Today, AI encompasses a diverse set of techniques used to enable intelligent behavior in machines. Core disciplines that contribute to modern AI research include computer science, mathematics, statistics, linguistics, psychology and cognitive science, and neuroscience. Significant AI approaches used today involve statistical classification models, machine learning, and natural language processing. Classification methods are widely applicable to problems in various domains like healthcare, such as informing diagnostic or treatment decisions based on patterns in data. Dean and Goldreich, 1998, define ML as an approach through which a computer has to learn a model by itself from the data provided but no specification on the sort of model is provided to the computer. They can then predict values for things that are different from the values used in training the models. NLP looks at two interrelated concerns, the task of training computers to understand human languages and the fact that since natural languages are so complex, they lend themselves very well to serving a number of very useful goals when used by computers.
