Sign Up

Have an account?

Sign In

Don't have account,

Forgot Password

Lost your password? Please enter your email address. You will receive a link and will create a new password via email.


Have an account?

Sorry, you do not have permission to ask a question, You must login to ask a question.

Need An Account,

Please briefly explain why you feel this question should be reported.

Please briefly explain why you feel this answer should be reported.

Please briefly explain why you feel this user should be reported.

Search
Ask A Question

Science & Technology

Share
Followers
1k Answers
2k Questions
Home/Science & Technology/Page 29

Mains Answer Writing Latest Questions

Khushi
  • 0
KhushiBegginer
Asked: July 17, 2024In:

Ai

  • 0

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

    See less
Khushi
  • 0
KhushiBegginer
Asked: July 17, 2024In:

Ai

  • 0

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.

    See less
[Deleted User]
  • 0
[Deleted User]
Asked: July 17, 2024In: ,
  • 0

  1. Anita Dhruw
    Anita Dhruw Legend

      1. Meta-learning As we have seen in previous sections of this tutorial There is an interesting field of study known as Meta-learning which trains a model on some set of task using only minimal amounts of information so that learning happens very rapidly from just very few examples as possibleRead more

     

    1. Meta-learning

    As we have seen in previous sections of this tutorial There is an interesting field of study known as Meta-learning which trains a model on some set of task using only minimal amounts of information so that learning happens very rapidly from just very few examples as possible.
    -Popular Methods: MAML, Prototypical Network, Matching Network

    2. Data Augmentation:

    -Increase the variety of training data: To derive more invariant features, the model is trained on augmented training data with large population and variation. Techniques include:
    -Image Augmentation: It helps distort images through rotation, flipping of images as well as distorting the size of images and adding noise to images as well.
    -Text Augmentation: This is carried out by synthesizing with synonyms, back translating and paraphrasing.
    -Generative Models: Employing Generative Adversarial Networks (GANs) to develop artificial data which resemble near real example cases.

    3. Transfer Learning:

    -Leverage Prior Knowledge: Start with a pre-trained model on large data sets such as the ImageNet image data or the BERT text data. Models of such a type have already acquired general features, in the presence of which new sets of parameters can be immediately updated to solve several particular few-shot learning tasks.
    -Fine-tuning: Fine-tune the pre-trained model with a limited number of labeled examples from the target task.

    5. Self-Supervised Learning:

    -Learn from Unlabeled Data: Pseudo Train models on a large amount of data with no labels to manage to get beneficial representations. This makes the models learn the general features and allows them to generalize on new tasks with few of the annotated datasets.

    -6. Attention Mechanisms:

    -Attention Mechanisms Focus on Relevant Information: Due to the attentiveness of the models, they can pay attention to different parts of input data and, thereby, their capability to learn from limited data.

     

    Key Considerations:

    -Dataset Bias: Pay close attention to the aspect of implicit bias in the training data because and this is a key factor that affects most of the few-shot learning models.
    Evaluation Metrics: Accurately analyse the few-shot learning model in conjunction with the evaluation metrics such as few-shot accuracy, and generalisability.

     

    See less
[Deleted User]
  • 0
[Deleted User]
Asked: July 17, 2024In: ,
  • 0

questionscience and technology
  1. Archi Kumar
    Archi Kumar Begginer

    Material Science has made a lot of advancement in various areas. As population increases, energy consumption is also increasing. By 2050, it is estimated that 30 TW of energy will be required to cater the needs of future generation. This large disparity can be achieved by renewable resources presentRead more

    Material Science has made a lot of advancement in various areas. As population increases, energy consumption is also increasing. By 2050, it is estimated that 30 TW of energy will be required to cater the needs of future generation. This large disparity can be achieved by renewable resources present in nature.

    Solar cells is one of the better alternatives that can provide better lifetime and more optimum usage. This is also used in photovoltaics and semiconductors. This energy resource makes better management and sustainable growth in various industries. It is an eco-friendly option and a renewable resource of energy.

    Nanotechnology is one of the great advancements today. This can be used to make better semiconductors. The biggest advantage of these semiconductors is the leverage on energy requirements. A user can make semiconductors according to his requirement and can change it by changing its shape.

    Due to less distance between electron and hole pairs, these semiconductors are a part of improved technology that help in reaching requirements better and are eco-friendly.

    See less
SURYA M 717822I258
  • 1
SURYA M 717822I258Begginer
Asked: July 17, 2024In:
  • 1

How is AI being used in creative fields such as art, music, and literature, and what are the limits of AI in terms of replicating human creativity and intuition?

  1. Shekhar jaat
    Shekhar jaat Begginer

    AI is increasingly being used in creative fields such as art, music, and literature, and it's fascinating to explore the possibilities and limitations of AI-generated creativity. Art: Generative Adversarial Networks (GANs): AI algorithms can generate artworks that mimic human styles, like Van Gogh'sRead more

    AI is increasingly being used in creative fields such as art, music, and literature, and it’s fascinating to explore the possibilities and limitations of AI-generated creativity.

    Art:

    1. Generative Adversarial Networks (GANs): AI algorithms can generate artworks that mimic human styles, like Van Gogh’s brushstrokes or Monet’s watercolors.
    2. Neural Style Transfer: AI can transfer styles from one image to another, creating unique and fascinating combinations.
    3. AI-assisted Art: Tools like Adobe Fresco and Prisma allow humans to collaborate with AI to create stunning artworks.

    Music:

    1. Algorithmic Composition: AI can generate music in various styles, from classical to pop, using algorithms and machine learning models.
    2. Music Generation: AI-powered tools like Amper Music and AIVA can create original songs, jingles, or soundtracks.
    3. Collaborative Music: Humans can co-create music with AI, like in the case of the AI-powered band “Daddy’s Car”.

    Literature:

    1. AI-powered Writing Assistants: Tools like Grammarly and ProWritingAid offer suggestions for sentence structure, grammar, and style.
    2. Content Generation: AI can generate articles, blog posts, or even entire books based on given topics and styles.
    3. AI-assisted Storytelling: AI can help with plot development, character creation, or even entire storylines.

    Limits of AI in Creative Fields:

    1. Lack of Human Empathy: AI lacks the emotional intelligence and empathy that humans take for granted.
    2. Limited Understanding of Context: AI may not fully comprehend the nuances of human culture, history, or context.
    3. Inability to Create Original Ideas: While AI can generate creative works, it often builds upon existing knowledge and concepts, rather than introducing completely new ideas.
    4. Dependence on Training Data: AI’s performance is only as good as the data it’s trained on, which can be biased or limited in scope.
    5. Lack of Human Judgment: AI may not possess the same level of critical judgment and taste as humans when evaluating artistic merit.

    The Future of AI in Creative Fields:

    1. Hybrid Collaboration: Humans and AI will increasingly collaborate on creative projects, leveraging each other’s strengths.
    2. AI-assisted Tools: As AI becomes more advanced, we’ll see more sophisticated tools that augment human creativity rather than replacing it.
    3. New Forms of Art: The intersection of human creativity and AI will likely give rise to entirely new forms of art, music, and literature.
    See less
Josepaul Jenson
  • 0
Josepaul JensonBegginer
Asked: July 17, 2024In: , , ,
  • 0

I’m trying to collect suggestions from the users on how to improve the user experience in Mains Answer Writing? This could be essential for the growth of the platform?

improvementsmains answer writingquestionsuggestions
  1. Diksha Kardam
    Diksha Kardam Begginer
    This answer was edited.

    Writing mains answer or any other exam is the most difficult and hectic part of the big opportunities but after clearing the exam you are the most proud person of this world and that feeling is out of the world. There are some important tips for writing these exam: Keep calm: First of all you have tRead more

    Writing mains answer or any other exam is the most difficult and hectic part of the big opportunities but after clearing the exam you are the most proud person of this world and that feeling is out of the world. There are some important tips for writing these exam:

    1. Keep calm: First of all you have to be keep calm while writing this exam don’t panic and feel nervous write your exam with the cool mind because nervousness  is the biggest Foe of our crucial opportunities.
    2. Make proper outline: While writing this exam, you should make proper outlines of every question for better understanding.
    3. Be clear: You must write the answer with clear understanding and meaning. Be clear with your words and what you want to write in the answer.
    4. Time management: You should manage your time in the exam for every question and for this you must practice of solving paper to maintain your speed.
    5. Use of examples:  You can use as much as examples in your answer for better understanding and it also give a good impact to your answers.
    6. Practice PYQs: You should practice previous question papers for the better understanding of the pattern of the paper and it also enhance your confidence to give exam properly.
    See less
Josepaul Jenson
  • 0
Josepaul JensonBegginer
Asked: July 17, 2024In: ,
  • 0

What do you think will be the future of Blockchain Technology? Will it be possible to efficiently reconstruct systems currently working in Web2 technology into Web3 technology? Share your insights.

blockchainfuturequestiontechnology
  1. Mrunali Ghadge
    Mrunali Ghadge Begginer

    The future of blockchain technology is promising, with potential to revolutionize numerous sectors. Blockchain's core strengths—decentralization, transparency, and security—make it a powerful tool for reshaping how we conduct transactions, manage data, and enforce trust. As for transitioning systemsRead more

    The future of blockchain technology is promising, with potential to revolutionize numerous sectors. Blockchain’s core strengths—decentralization, transparency, and security—make it a powerful tool for reshaping how we conduct transactions, manage data, and enforce trust.

    As for transitioning systems from Web2 to Web3, it’s feasible but not without challenges. Web3 aims to create a more user-centric internet, leveraging blockchain for decentralized applications (dApps). This shift means moving from centralized servers to decentralized networks, which can enhance privacy and data ownership.

    However, the transition involves significant hurdles. Scalability remains a major concern; current blockchain networks often struggle with handling large volumes of transactions quickly. This is where innovations like sharding and layer-2 solutions come in, offering ways to enhance scalability and efficiency.

    Integration with existing systems also poses a challenge. Many Web2 systems are deeply entrenched, with vast amounts of legacy data and infrastructure. Migrating these to a decentralized framework will require robust interoperability solutions and considerable effort.

    Despite these challenges, the potential benefits of Web3 are compelling. Enhanced security, reduced reliance on intermediaries, and greater control over personal data could drive widespread adoption. As blockchain technology continues to mature, we may see more efficient and user-friendly ways to bridge the gap between Web2 and Web3, paving the way for a more decentralized internet.

    See less
Josepaul Jenson
  • 0
Josepaul JensonBegginer
Asked: July 17, 2024In: , , , , , , ,
  • 0

To all the Engineering Graduates or students who are pursuing Engineering Degree in India, what suggestions would you like to provide to the younger generations who are interested in joining to an Engineering Course in India?

engineeringindiaquestionstudentssuggestions
  1. Paramita Sahu
    Paramita Sahu Begginer

    To all aspiring engineering students in India, here are some valuable suggestions to help you make the most of your engineering education and prepare for a successful career: Choosing the Right Institution Research Thoroughly: Investigate the reputation, faculty, infrastructure, and placement recordRead more

    To all aspiring engineering students in India, here are some valuable suggestions to help you make the most of your engineering education and prepare for a successful career:

    Choosing the Right Institution

    1. Research Thoroughly:
      • Investigate the reputation, faculty, infrastructure, and placement records of various engineering colleges.
      • Consider institutions accredited by the National Board of Accreditation (NBA) or those with a good National Institutional Ranking Framework (NIRF) ranking.
    2. Specialization Selection:
      • Choose a branch of engineering that aligns with your interests and career goals. Common branches include Computer Science, Mechanical, Electrical, Civil, and Electronics.
      • Explore emerging fields like Artificial Intelligence, Data Science, and Cybersecurity if they interest you.

    Academic Excellence

    1. Strong Foundation:
      • Focus on building a strong foundation in core subjects like mathematics, physics, and chemistry during your pre-university education.
      • Strengthen your understanding of fundamental concepts, as they will be crucial in your engineering coursework.
    2. Consistent Effort:
      • Maintain a consistent study routine and keep up with your coursework. Engineering curriculum can be demanding, so regular study is essential.
      • Attend classes regularly and participate actively in lectures and lab sessions.

    Practical Experience

    1. Hands-On Learning:
      • Engage in practical projects and lab work to apply theoretical knowledge.
      • Participate in workshops, internships, and industrial visits to gain real-world experience.
    2. Join Clubs and Societies:
      • Become a member of engineering clubs, societies, or technical teams in your college. These groups often work on innovative projects and participate in competitions.
      • Take part in hackathons, coding contests, and robotics competitions to enhance your skills.

    Skill Development

    1. Programming and Software Skills:
      • Learn programming languages relevant to your branch, such as Python, C++, Java, or MATLAB.
      • Familiarize yourself with industry-standard software and tools, such as CAD for mechanical engineering or circuit design software for electronics.
    2. Soft Skills:
      • Develop communication, teamwork, and leadership skills. These are crucial for success in both academic and professional settings.
      • Participate in group discussions, presentations, and extracurricular activities to enhance these skills.

    Networking and Mentorship

    1. Build a Network:
      • Connect with seniors, alumni, and industry professionals. Networking can provide valuable insights and open up opportunities for internships and jobs.
      • Attend seminars, webinars, and industry conferences to stay updated on the latest trends and to network with experts.
    2. Seek Mentorship:
      • Find mentors among your professors, seniors, or industry professionals. A good mentor can provide guidance, support, and advice for your academic and career growth.

    Career Planning

    1. Explore Career Options:
      • Research various career paths available to engineering graduates, including higher studies, research, entrepreneurship, and industry roles.
      • Consider the long-term prospects and growth opportunities in different fields.
    2. Placement Preparation:
      • Prepare for campus placements by practicing aptitude tests, technical interviews, and group discussions.
      • Create a strong resume highlighting your skills, projects, internships, and achievements.

    Continuous Learning

    1. Stay Updated:
      • Keep abreast of the latest developments and technologies in your field. Follow industry news, subscribe to technical journals, and join online forums.
      • Take online courses, certifications, and attend workshops to enhance your knowledge and skills.
    2. Adaptability:
      • Be open to learning new technologies and adapting to changing industry trends. The engineering field is constantly evolving, and staying adaptable is key to long-term success.

    Personal Well-being

    1. Balance and Well-being:
      • Maintain a healthy work-life balance. Ensure you allocate time for hobbies, exercise, and relaxation.
      • Manage stress through effective time management and by seeking support when needed.
    2. Ethics and Integrity:
      • Uphold ethical standards in your academic and professional endeavors. Integrity and ethical behavior are highly valued in the engineering profession.
    See less
ROHIT RANDIVE
  • 0
ROHIT RANDIVEBegginer
Asked: July 17, 2024In:
  • 0

How do machine learning algorithms differ from traditional programming?

question
  1. Sirri Ayongwa
    Sirri Ayongwa Begginer

    Machine learning algorithms differ from traditional programming in their approach to problem-solving. In traditional programming, a developer writes explicit instructions to solve a problem using predefined rules and logic. The input data is processed by these rules to produce the output. In contrasRead more

    Machine learning algorithms differ from traditional programming in their approach to problem-solving. In traditional programming, a developer writes explicit instructions to solve a problem using predefined rules and logic. The input data is processed by these rules to produce the output.

    In contrast, machine learning algorithms learn from data. Instead of being explicitly programmed, they are trained on a dataset, identifying patterns and making predictions or decisions based on this training. The algorithm adjusts itself based on the data it processes, allowing it to improve over time without explicit reprogramming. This approach is particularly effective for complex tasks like image recognition, natural language processing, and predictive analytics, where writing explicit rules is impractical or impossible.

    Overall, traditional programming relies on human-crafted rules, while machine learning leverages data-driven learning to derive insights and make decisions.

    See less
Load More Questions

Resources & Suggestions

Mains Answer Writing Latest Articles

On: April 18, 2025

Daily Answer Writing Practice Questions (18 April 2025)

Do you agree with the claim that indecision and risk aversion are prevalent issues in Indian bureaucracy? Support your answer with logical reasoning. (150 words) ऐसा कहा जाता है कि भारतीय नौकरशाही में अनिर्णय और जोखिम से बचने की प्रवृत्ति ...

On: April 18, 2025 Comments: 0

Strengthening India’s Cyber Defence

Rising Threats Digital Era Challenges: 2024 marks a significant rise in digital threats, particularly from AI and cyberattacks. Key Issues: Disinformation campaigns. Cyber fraud affecting daily life. Current Major Cyber Threats Ransomware Rampage: Over 48,000 instances of WannaCry ransomware detected ...

On: April 18, 2025 Comments: 0

भारत की साइबर सुरक्षा

बढ़ते खतरे कृत्रिम बुद्धिमत्ता (AI) और साइबर हमले: 2024 में AI और साइबर हमलों के खतरे में वृद्धि। महत्वपूर्ण अवसंरचना पर हमले: डिजिटल हमलों और दुष्प्रचार अभियानों की संभावना बढ़ी है। प्रमुख साइबर खतरें रैनसमवेयर का प्रकोप: 48,000 से अधिक ...

Explore Our Blog