Artificial intelligence and big data is a very popular area. Many of the areas of interest of friends would ask: learning artificial intelligence or a large data which is better? These two fields are similar seems to be related. Before learning, you should know their respective research and content should also know your favorite places.
What is the relationship between big data and artificial intelligence that?
The popularity of big data is mainly due to the development of accounting machine technology, in the meantime, to improve the ability of large data access big data is a condition for effective use. Classification and analysis of massive data on the nature of big data. Simply put, it's like a sieve to pick something also needed. After the data classification, data analysis, such as data representing the proportion of Class A, Class B and other accounting data ratio. Finally, it forms a variety of data reports, can be used to sense the data contained in the manual analysis.
Artificial intelligence relies on the presence of large data.
It can be said, big data promotes reliance on large data artificial intelligence. Today, people are doing big data artificial intelligence, clouds client, smart supermarkets, unmanned robot car and new phone. Behind this is a product of artificial intelligence and large data
on a smart phone to the robot for example, in addition to the interior of the system, the data in the above crawling, between the feedback configuration, the user and external mechanisms, it is in large data blessing following completion, with the combination of artificial intelligence and big data, it can be widely used in banking, prudential, traditional areas of Securities dealers, robot stock speculation.
So big data and artificial intelligence What is the difference?
First, two important points:
Big Data Java
AI python
Learning or artificial intelligence, big data, you first need to master an accounting machine programming language, we all know there are many accounting machine programming languages, such as: R, C ++, JAVA and so on. Big data and artificial intelligence using Java, using Python.
Choice of development tools sometimes depends on the particular constraints or personal preferences. Now, Python has become the top-level programming language. Compared with the compiler, statically typed languages (such as C, C ++ and Java), the ability to Python developers increased by several times.
Large data need to be useful in prior sorting data structures and integration of the original input, and the processed data is the AI *** outputs, i.e. intelligent. This creates a fundamental difference between the two.
AI is an accounting method, which allows the machine to perform cognitive functions, such as acting on the input or in response, similar to human practice. Big Data is a traditional accountant. It is not based effects and action, it is only seeking to effect.
Big Data learning or artificial intelligence better?
In fact, artificial intelligence and big data can work well together, there is no contradiction between the two. It is like when you develop a robot phone, and then test the robot phone really easy to use, not just only to test his reflection, artificial intelligence data needed to build intelligence, machine learning in particular. For example, the phone machine learning various dialects, we need to import different, then surgery, and then a big blessing to the following data to identify them.
Learn still based on your love and your plans for the future, but it is worth knowing that the future must be the era of artificial intelligence. Perhaps AI will take away the work of big data engineer in the future. Due to in-depth study, the machine can help people select the data and get some work done. AI can help people to "play" the commercial value of the data.
Countries also support the development of artificial intelligence. Chinese demand for artificial intelligence professionals up to one million, higher than the wage level of artificial intelligence engineer of large data engineer.
Artificial intelligence is not as deep as we thought. Many people on the market now need to use existing algorithms, rather than developing new algorithms.
In short, the face of intense work on the market, we can not blindly choose. We must proceed from their actual recognition of our love and work plan.