We are all familiar with the concept of "artificial intelligence". After all, this word often appears in popular movies, such as "Terminator", "The Matrix", and "Mechanic Ji". But recently you may also often hear other terms, such as "machine learning", which are sometimes used interchangeably with artificial intelligence. The impact of artificial intelligence on our society and the future is unpredictable. To be sure, with the progress and continuous development of artificial intelligence and the Internet of Things, the impact will be profound.
In the era of artificial intelligence, learning programming has become a rigid need in society, and elementary school programming has become the top priority of leading the era of artificial intelligence. Therefore, many programming education institutions have sprung up like mushrooms. Among them, the only children's programming education institution invested by New Oriental-Extreme Ke Chenxing adopts a game-based interactive teaching method, spiraling upwards and seamlessly connecting the curriculum system, and professional teachers devote themselves to creating visual module courses to cultivate children's logical thinking ability, imagination and creativity, and integration for children aged 6-16 across the country. Disciplinary ability. The Geek Morning Star learning philosophy aims to start from the essence and let children use programming as a tool to perceive the world. Guide students step by step to break through learning difficulties, form programming thinking imperceptibly, and realize the ability to write code independently. Geek Morningstar’s characteristic curriculum design allows students to communicate with the teacher at zero distance through the form of game-based interactive teaching, which reduces children’s unfamiliarity with programming at the root. Innovative theme games and easy-to-master learning methods allow children to quickly master the power of programming in a relaxed and pleasant learning environment.
Those things about artificial intelligence
The concept of "artificial intelligence" was first proposed by computer scientist John McCarthy in 1956. Refers to a machine with human intelligence characteristics when processing tasks. Including the ability to organize and understand language, recognize objects and sounds, and learn and solve problems.
We can divide artificial intelligence into two aspects: broad sense and narrow sense. In a broad sense, it includes all the above-mentioned characteristics of human intelligence. In a narrow sense, it refers to artificial intelligence in certain fields, and can be used to the extreme in these fields, but it is only limited to this field. For example, a machine that is extremely good at recognizing images, but performs poorly in other areas, this is artificial intelligence in a narrow sense.
Those things about machine learning
Machine learning is a way to realize artificial intelligence. Soon after the emergence of the concept of artificial intelligence, Arthur Samuel proposed the concept of "machine learning" in 1959, that is, "(computers) can learn independently without special programming." You can implement artificial intelligence without using machine learning, but that means writing millions of lines of code with complex rules.
Therefore, unlike traditional programming that makes computers complete tasks with clear instructions, machine learning uses "training" to make it learn how to complete tasks. "Training" involves loading a large amount of data into the model, and can automatically adjust and improve the algorithm. For example, machine learning has been used to improve computer vision (the ability of machines to recognize objects through images or videos). People collect hundreds of thousands or even millions of pictures and mark them one by one. For example, humans can mark pictures with cats in them, but not those without cats. Then, the algorithm tries to build a model that can accurately mark pictures containing cats like people. Once a certain degree of accuracy is reached, we can assume that the machine has now "learned" to recognize what a cat looks like.
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