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• Engaging in public discussions around the social and ethical implications. Foster open discussions with the public
                about the benefits and risks of Generative AI. Collaborate with policymakers, ethicists, and other stakeholders to
                develop guidelines and regulations that ensure AI is used in socially beneficial ways.
              All  these  points  ensure  the  responsible  use  of  Generative  AI.  By  emphasising  ethics,  creating  trust,  limiting
              negative repercussions, defining legislation, and encouraging innovation, we maximise generative AI’s potential
              and use it in ways that benefit society.


                        At a Glance


                    • AI-generated images are created using AI algorithms.
                    • Artificial intelligence shows inconsistencies if observed closely, although it tries to piece together its creations from
                    the original work.
                    • AI-generated images may include elements that seem unrealistic or improbable, such as impossible perspectives,
                    mismatched colors, or objects that defy physics.
                    • In a supervised learning model, a labelled dataset is given to the machine.
                    • A labelled dataset is the information which is tagged with identifiers of data.
                    • In Generative Modelling there is no labelled dataset, and the model can generate structured data from the Random
                    Noise dataset.
                    • A "random noise dataset" typically refers to a collection of data points or samples where each data point is generated
                    randomly.
                    • Generative Artificial Intelligence also called Gen AI, refers to the algorithms that generate new data that resembles
                    human-generated content, such as audio, code, images, text, simulations, and videos.
                    • GANs are neural networks that work to produce fresh data.
                    • Variational Autoencoders (VAEs) produces fresh data, learn the distribution of the data and then sample from it.


                                                           Exercise




                                                SECTION A (Objective Type Questions)
                        uiz

              A.  Tick ( ) the correct option.
                  1.  ……………………….  are  unpredictable  fluctuations  and  disarranged  data  which  makes  it  impossible  to  identify  target
                    patterns or relationships in it.
                    a.  Deepfake                                      b.  Random Noise Dataset
                    c.  Gen AI                                        d.  Discriminative Modelling
                  2.  Which of the following is not a generative AI tool?

                    a.  Galileo                                       b.  CodeGPT
                    c.  Magic AI                                      d.  Vista AI
                  3.  ………………………. are neural networks that work to produce fresh data.
                    a.  Deepfake                                      b.  Gen AI
                    c.  GANs                                          d.  VAE

                  4.  What is a primary advantage of using Autoencoders?
                    a.  Generating realistic videos                   b.  Creating noise-free images
                    c.  Handling sequential data                      d.  Generating new text




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