What are the four elements of an artificial intelligence system?

The four elements of artificial intelligence systems are: 1. Big data; the intelligence of artificial intelligence is contained in big data. 2. Computing power; provides basic computing power support for artificial intelligence. 3. Algorithm; the fundamental way to realize artificial intelligence and an effective method to mine data intelligence. 4. Scenario; preprocess large amounts of data.

Analysis of four elements of artificial intelligence
Big data:
The intelligence of artificial intelligence is contained in big data.
Computing power:
Computing power provides basic computing power support for artificial intelligence.
Algorithm:
Algorithm is the fundamental way to realize artificial intelligence and an effective method to mine data intelligence.
Scenario:
Big data, computing power, and algorithms are used as input, and only when output in actual scenarios can the actual value be reflected.
Let’s give a very vivid analogy: If we take cooking as our scenario, then big data is equivalent to the ingredients needed for cooking, computing power is equivalent to the gas/electricity/firewood needed for cooking, and the algorithm is equivalent to the cooking method and seasoning.

1) Big Data
In today’s era, big data is generated all the time. Data accumulated from mobile devices, cheap cameras, ubiquitous sensors, and more. These data are in various forms, and most of them are unstructured data. If it needs to be used by artificial intelligence algorithms, a large amount of pre-processing process is required.

2) Computing power
The development of artificial intelligence has put forward higher requirements for computing power. The following is a comparison of the computing capabilities of various chips. Among them, GPU is the most widely used chip in the field of artificial intelligence, ahead of other chips. Both GPU and CPU are good at floating point calculations. Generally speaking, the GPU's ability to do floating point calculations is about 10 times that of the CPU. In addition, the deep learning acceleration framework is optimized on the GPU to once again improve the computing performance of the GPU, which is beneficial to accelerating the calculation of neural networks. For example: cuDNN has customizable data layout, supports flexible dimensional ordering of four-dimensional tensors, strides and sub-regions, used as input and output of all routines. Matrix operations are implemented in the convolution operation of the convolutional neural network, while reducing memory and greatly improving the performance of the neural network.

3) Algorithm
The mainstream algorithms are mainly divided into traditional machine learning algorithms and neural network algorithms. Neural network algorithms have developed rapidly, and in recent years the development of deep learning has reached a climax.

4) Scenarios
Classic application scenarios of artificial intelligence include:
1. User portrait analysis
2. Risks based on credit scores Control
3. Fraud detection
4. Robo-advisor
5. Intelligent review
6. Intelligent customer service robot
7. Machine translation
8. Face recognition

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