It is well known that the current artificial intelligence technology is widely used in all walks of life, and has a certain improvement effect on these industries. If you want to apply artificial intelligence technology more deeply, you need to have a more thorough understanding of artificial intelligence technology.
The ability of AI to process large amounts of data makes current AI technologies highly important for retail, applications requiring prediction of causal drivers, and risk assessment applications in banking. There are still many challenges facing AI systems, and these challenges have a profound impact on the marketing of AI applications.
Current challenges in the application of AI technologies include annotating training data, obtaining extensive and comprehensive data, interpreting output results, and universality of learning.
In terms of marking training data, AI systems at present mainly adopt supervised learning mode, which requires a lot of time, manpower or funds to mark data in advance. Therefore, it has become a disadvantage for the promotion and introduction of AI systems. At present, in view of this disadvantage, many researchers have launched new calculation models one after another, hoping to gradually achieve the goal of allowing data to be marked automatically, so as to greatly reduce the input of manpower and time.
In terms of large and comprehensive data acquisition, it is not easy to obtain sufficient quantity and quality of data for many industries, so it will be more difficult to import such data into AI systems for industries where it is difficult to obtain such data.
The interpretation of the output results is difficult to achieve by AI systems at present. The reason is that the AI system can calculate the result, but it cannot explain how to get the result step by step, so the need to explain the result has not been met so far.
As for the general part of learning, it is because current AI models often have trouble moving learning experiences from Class A to class B. This means that companies need to invest A lot of money in training new models, even if there are some similarities between A and B.