Since artificial intelligence is closely related to big data, it can only be handled better through clear structured data, thus improving people's lives. Fortunately, people are gradually understanding the needs behind the development of artificial intelligence. This is why people see improvements in the way that data scientists work in terms of funding, wages, tools, and equipment.
With the development and progress of technology, the amount of data has grown exponentially, which is not surprising. If people can still handle 0.1ZB data in 2005, then this figure has exceeded 20ZB. Even by 2020, the data volume will reach 47ZB. In addition to the large number, the data is also faced with the problem that most of it is unstructured data, and these incomplete or inaccurate data do not have any benefit for the booming artificial intelligence and humans.
People can only deal with 10% of structured data today, and the rest is a lot of unmarked information, and machines can't be used in a constructive way. For example, email is unstructured data, and content such as spreadsheets is considered structured data that is marked and can be successfully scanned by the machine.
This may not seem like a problem, but if people expect artificial intelligence to be better used in healthcare, driverless cars, and family life, this will require clean and orderly data. Ironically, people are already very good at creating content and data, but have not found a way to accurately use data to meet people's needs.
Data scientists are also working hard
Data science is one of the areas where a large amount of data has been accumulated over the past few years. It is natural that more and more data scientists are committed to solving this chaos problem. However, a recent survey shows that, contrary to people's opinions, data scientists spend less time building algorithms and mining data patterns, but instead carry out so-called digital clean-up work, which is cleaning and organizing data. As people have seen, these data are certainly not conducive to the development and application of artificial intelligence with a bright future.
In predicting the development of artificial intelligence, people obviously did not consider the fact that while machines can successfully replace a few data scientists who mine data for patterns, they may not be able to replace the vast majority of scientists who are committed to research data, and they are large. Part of the time is collecting, cleaning and organizing these data. Of course, it's best to collect data in a more holistic manner from the beginning, rather than allocating too much time and resources to trace and repair the data. Fortunately, leaders in the field of artificial intelligence have slowly reached this consensus, using their skills and influence to change the direction of data science and link it with artificial intelligence.
Artificial intelligence cannot catch up with humans at present
People have heard reports that artificial intelligence surpassed humans in certain aspects. For example, the world’s highest-level Go master was defeated by Google’s AlphaGo artificial intelligence. However, this can only show that artificial intelligence can achieve amazing results in niche missions, but its overall capabilities are still unmatched by human capabilities. Artificial intelligence cannot handle many subtle, logical steps and measures at all.
The limitations of artificial intelligence are even more obvious in dealing with financial filings and laws and regulations. Its problems are the same as everywhere else. As long as artificial intelligence machines do not provide structured data, such as standardized contracts, artificial intelligence can be very confused. This means that data scientists are still needed to solve this problem.
Team work makes AI more effective
The cost of hiring highly qualified data analysts is high, which makes the progress in this area more difficult. The key is to collect and model by adopting technologies that simplify the process.
Another key aspect is that multiple departments need to work together to solve the problems caused by big data. Financial and technical experts need to work together to correctly identify potential defects in the data they collect from the outset. The way these experts solve the problem should also be registered so that they can be successfully copied by the machine. The goal is to create a quality assurance algorithm to determine past simulation results related to errors. The more models people can create, the less space for data errors and violations.
Without big data, artificial intelligence cannot survive
No matter what the direction of artificial intelligence development, it may bring more benefits or disadvantages to human beings, but one thing is certain: artificial intelligence without big data will eventually achieve nothing. People have already got a lot of examples from daily life. These examples are probably taken for granted, which proves the necessity of artificial intelligence. Take Cortana or Siri as an example. They can understand people's questions and questions simply because they have acquired endless information to help them understand people's natural language. Google search engine seems to have become an omniscient force and it is well known to everyone because people have a large number of logs every day on their search engine. For this reason, companies can also make accurate reports, such as those that can use the relevant tools to identify websites, thanks to the cleanliness of the data initially collected.
Since artificial intelligence is closely related to big data, it can only be handled better through clear structured data, thus improving people's lives. Fortunately, people are gradually understanding the needs behind the development of artificial intelligence. This is why people see improvements in the way that data scientists work in terms of funding, wages, tools, and equipment.
This awareness is gradually gaining popularity on a global scale, enabling companies and experts to collaborate with each other to collect data more effectively, establish models that can further help machines clean and structure data, and lay the foundation for future development. Understanding where the problems of artificial intelligence and big data lie means that their problems have been solved by half.
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