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Environmental Impact Assessment ( EIA ) is a process of evaluating the likely environmental impacts of a proposed project or development, taking into account inter-related socio-economic, cultural and human-health impacts, both beneficial and adverse. Creation of this system is vital to conform socio-economic development projects to environmental safety and thereby ensure sustainable economic development. ... It also enables us in carrying out environmental cost-benefit analysis of projects at an initial stage. The draft seems to favour the industries and seems to be largely neglecting the balance between sustainable development and environment protection. The Union government, on the contrary, argues that the new draft will reinforce transparency and expedite the process. EIA covers projects such as mining of coal or other minerals, infrastructure development, thermal, nuclear and hydropower projects, real estate and other industrial projects. The projects are assessed based on their

In addition to AI, there are several other closely related topics that are good to know at least by name. These include machine learning, data science, and deep learning.   Machine learning can be said to be a subfield of AI, which itself is a subfield of computer science (such categories are often somewhat imprecise and some parts of machine learning could be equally well or better belong to statistics). Machine learning enables AI solutions that are adaptive. Deep learning is a subfield of machine learning, which itself is a subfield of AI, which itself is a subfield of computer science. We will meet deep learning in some more detail in Chapter 5, but for now let us just note that the “depth” of deep learning refers to the complexity of a mathematical model, and that the increased computing power of modern computers has allowed researchers to increase this complexity to reach levels that appear not only quantitatively but also qualitatively different from before. As

In brief, a robot is a machine comprising sensors (which sense the environment) and actuators (which act on the environment) that can be programmed to perform sequences of actions. People used to science-fictional depictions of robots will usually think of humanoid machines walking with an awkward gait and speaking in a metallic monotone. Most real-world robots currently in use look very different as they are designed according to the application. Most applications would not benefit from the robot having a human shape, just like we don't have humanoid robots to do our dishwashing but machines in which we place the dishes to be washed by jets of water.                                                   It may not be obvious at first sight, but any kind of vehicles that have at least some level of autonomy and include sensors and actuators are also counted as robotics. On the other hand, software-based solutions such as customer service chatbot, even if they are sometimes called `

As you have probably noticed, AI is currently a "hot topic": media coverage and public discussion about AI are almost impossible to avoid. However, you may also have noticed that AI means different things to different people. For some, AI is about artificial life-forms that can surpass human intelligence, and for others, almost any data processing technology can be called AI. To set the scene, so to speak, we'll discuss what AI is, how it can be defined, and what other fields or technologies are closely related. Before we do so, however, we'll highlight three applications of AI that illustrate different aspects of AI. We'll return to each of them throughout the course to deepen our understanding. Application 1. Self-driving cars Self-driving cars require a combination of AI techniques of many kinds: search and planning to find the most convenient route from A to B, computer vision to identify obstacles, and decision making under uncertainty to cope

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