Big data and artificial intelligence are not simple, both need a systematic learning process and long-term experimentation, the two are closely linked, there is no who is more difficult, because both have difficult to control the difficult point.

The development of Big Data has greatly contributed to the development of Artificial Intelligence, because data is the basis of intelligence, so from this point of view, the development of Big Data and Artificial Intelligence are bound to promote each other.

Big data and artificial intelligence are not simple, both require a systematic learning process and long-term experiments, the two are closely linked, it can be said that you have me, I have you. From the perspective of learning, it is recommended to start learning from big data, which will be smoother.

Big Data is equivalent to the massive amount of knowledge memorized and stored by the human brain from elementary school to university, which can only be digested, absorbed, and recreated to create greater value.

Artificial Intelligence is compared to a person absorbing a large amount of human knowledge, continuous deep learning, evolution to become a party of high people. Artificial intelligence is inseparable from big data, but also based on the cloud computing platform to complete the evolution of deep learning.

Artificial Intelligence is based on the support and collection of big data, used in the artificial set of specific performance and computing to achieve, big data is constantly collected, precipitation, classification and other data accumulation.

In contrast to numerous previous data analytics techniques, artificial intelligence technology is grounded in neural networks, while developing multi-layer neural networks that allow for deep machine learning. Compared with other traditional algorithms, this algorithm does not have redundant assumptions (for example, linear modeling needs to assume a linear relationship between the data), but rather completely use the input data to simulate and build the corresponding model structure by itself. This makes the algorithm more flexible and self-optimizing depending on the training data.