The article discusses the main approaches to the development and use of the task system for the first All-Russian Olympiad in artificial intelligence for schoolchildren. The first Olympiad in artificial intelligence for schoolchildren, held by the Ministry of Education of the Russian Federation, had distinctive features in the procedure of the competition and in competition tasks. These features can be used for comparative analysis and expert evaluation of intellectual competitions for schoolchildren held in Russia. The subjects of the Olympiad tasks are Natural Language Processing, Computer Vision, and Data Science.The Python programming language was used. The tasks were focused on the formation of an individual path for the student to prepare for the final stage of the Olympiad in artificial intelligence, i. e. thematic continuity was observed at all stages and the practical orientation of tasks using data sets was observed in the second and third rounds. For the assessment, a rating assessment was used (as in the professional community Kaggle.com) of the individual abilities of schoolchildren in the complex application of mathematical and digital skills in solving artificial intelligence tasks.The article may be useful for informatics teachers who include the topic in an advanced informatics course and teachers of additional education who prepare schoolchildren for Olympiads in programming.
The article discusses approaches to developing a system of Olympiad tasks, features of the event, as well as solving tasks of two stages of the first All-Russian Olympiad in artificial intelligence for 8th—11th grades, held in October-November 2021 by the Ministry of Education of the Russian Federation as part of the Federal Project "Artificial Intelligence" of the National Program "Digital Economy of the Russian Federation". The topics of the Olympiad tasks were Natural Language Processing and Computer Vision. At all stages of the Olympiad, the Python programming language with specialized libraries for solving artificial intelligence problems was used. The material with the analysis of tasks will be useful when teaching informatics at an advanced level at school, including when preparing schoolchildren to participate in the next Olympiad in artificial intelligence in an individual format.
The article discusses the possibility of including artificial intelligence topics in the informatics course at the level of secondary general education on the demonstration example of handwritten digits recognition using the Python 3.8 programming language and the TensorFlow package. A hands-on example of a classification problem using the classic MNIST (Modified National Institute of Standards and Technology) training example set is dealt with step-by-step.Most of the tools required for the work (language interpreter, basic libraries, and shell) are downloaded in the form of a single software distribution kit of Anaconda. The network is trained using different methods. When the model is trained, the optimization method, the loss function, and the metric for estimation are specified. Image processing by convolutional nets containing special layers, which "convolve" the image the way a bitmap filter does, is also demonstrated
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