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Khushi

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Mains Answer Writing Latest Questions

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KhushiBegginer
Asked: July 17, 2024In:
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Discuss the significance of the Directive Principles of State Policy in the Indian Constitution. How have these principles been used to promote social and economic development?­

upsc:features of indian constitution
  1. Arushi Vaishnav
    Arushi Vaishnav
    This answer was edited.

    Directive Principle of State Policy (DPSP) present in Part V of the constitution was included with the agenda of creating a welfarist state. The significance of DPSP is :- 1. It ensures socio-economic democracy with political democracy as preached by Dr. B.R. Ambedkar. 2. It tries to establish IndiaRead more

    1. Directive Principle of State Policy (DPSP) present in Part V of the constitution was included with the agenda of creating a welfarist state. The significance of DPSP is :-

    1. It ensures socio-economic democracy with political democracy as preached by Dr. B.R. Ambedkar.

    2. It tries to establish Indian state a welfare state by ensuring socio-economic justice.

    3. It creates accountability of state to its citizen.

    4. It act as guidelines for state to perform its welfarist function.

    5. It also focuses on environment protection, international peace and promote education.

    6. It focuses on societal welfare apart from individual rights mention in Part 3 of the constitution.

    There a various policy and programs of the government which reflects its application of DPSP such as :-

    1. MNREGA

    2. 73rd Amendment Act establish village panchayats

    3. ICDS Aanganwadi

    4. Mid day meal scheme

    5. Trade union act

    6. Pension scheme

    The current debate around Article 44 of DPSP where application of Uniform Civil Code (UCC) is suggested is a heated debate. There are plethora of such examples where government has took inspiration from DPSP and applied it not only for welfare but also for socio-economic development of its citizen.

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Khushi
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KhushiBegginer
Asked: July 17, 2024In:

Ai

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Can AI be used to predict and prevent cyber attacks?

artificial intelligence
  1. Manish Chauhan
    Manish Chauhan Begginer

    Yes, AI can be used to predict and prevent cyber attacks. AI algorithms can analyze patterns in network traffic, detect anomalies, and identify potential threats in real-time. Machine learning models can be trained on historical data to recognize the signatures of known attacks and to detect new, prRead more

    Yes, AI can be used to predict and prevent cyber attacks. AI algorithms can analyze patterns in network traffic, detect anomalies, and identify potential threats in real-time. Machine learning models can be trained on historical data to recognize the signatures of known attacks and to detect new, previously unseen threats. Additionally, AI can automate responses to threats, reducing the time it takes to mitigate potential cyber attacks.

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Khushi
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KhushiBegginer
Asked: July 17, 2024In:

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What are the key differences between machine learning and deep learning?

artificial intelligence
  1. surya vardhan
    surya vardhan Begginer

    Machine learning (ML) and deep learning (DL) are both subsets of artificial intelligence, but they differ in several key ways. ML uses a variety of algorithms like decision trees and support vector machines to make predictions based on data and often requires manual feature engineering. It works welRead more

    Machine learning (ML) and deep learning (DL) are both subsets of artificial intelligence, but they differ in several key ways. ML uses a variety of algorithms like decision trees and support vector machines to make predictions based on data and often requires manual feature engineering. It works well with smaller datasets and is computationally less intensive, making it more interpretable and easier to implement for simpler problems. DL, a subset of ML, uses neural networks with many layers to automatically learn features from large amounts of unstructured data. This requires significant computational power and large datasets but excels in complex tasks like image and speech recognition. DL models often outperform traditional ML models in these tasks but are less interpretable and more resource-intensive. Mainly deep learning problems are black box because the decision making is not seen to us,as their thousands of parameters are their for simplest of neural networks but the interactions between them cannot be interpreted or traced easily

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