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More the data, the better will be the analysis and the more accurate would be the prediction. Thus, AI requires
                 large amounts of data to find the latest trends and patterns. For example, I plan to organise a social gathering in
                 an open-air setup in the month of August in Delhi. I will have to look for a weather forecast for the same and also
                 look at the previous years' trends. So, data collection is the base for analyses and pattern recognition models. From
                 those patterns, predictive models can be made.
                 So, the data is the most important part of an AI machine. After the data is collected from different sources, the AI
                 machine analyses the data and then processes it.

                                                                                 #Experiential Learning

                                       GAME        01              Rock Paper and Scissors

                      Visit: https://next.rockpaperscissors.ai/ or scan the QR code to play the game.
                      The challenge here is to win 20 games against AI before AI wins them against you. Answer
                      the following questions after playing the game:
                      1.  Did you manage to win?
                      2.  What was the strategy that you applied to win this game against the AI machine?
                      3.  Was it different to play Rock, Paper & Scissors with an AI machine as compared with a human?
                      4.  What approach was the machine following while playing against you?
                      It is a game based on data statistics domain of AI where the machines try to predict the next move of
                      the participant. It is made on similar rules as a basic rock, paper and scissors game we played in our
                      childhood days. Learning from the previous moves of the player the machine tries to win ahead with
                      the participant. With every game, the machine is learning from your moves, and able to play it better.



                 Computer Vision

                 We use our vision to sense and identify objects around us. In the same way, AI machines use the computer vision
                 domain to understand their surroundings. Computer Vision (CV) is the domain of artificial intelligence that is used
                 to train computers to read, process, and analyse visual data in the same way as humans do. The type of data in CV
                 is images and videos. The AI machine collects data from digital images and videos, and then process the data to
                 identify the objects in the image/video. For example, a self-driven car scans live objects, like traffic signals, analyses
                 the scanned image, and then creates the 3D version of it. After that, the car decides whether to run or stop. Some
                 other applications of CV in AI are facial lock in smartphones, unusual behaviour detection, object classification,
                 home security systems, office security systems, and drone-based surveillance systems.


                                                                                 #Experiential Learning

                                       GAME         02                      Quick Draw


                      Quick Draw is a game developed by Google that uses machine learning to guess what you're drawing.
                      It prompts you to draw an object or concept within 20 seconds, and as you draw, a neural network
                      attempts to recognise your doodle in real-time.
                      Visit: https://quickdraw.withgoogle.com/ or scan the QR code to play the game. The
                      challenge here is to draw 6 doodles within the time limit of 20 seconds each.
                      Answer the following questions after playing the game:
                      1.  Did you manage to win?
                      2.  What was the strategy that you applied to win this game?
                      3.  Was the neural network able to identify all the doodles created by you?


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