What is the "bottleneck" of artificial intelligence and how does it affect technological progress?

The "bottleneck" of artificial intelligence (AI) refers to some insurmountable technical difficulties and limitations encountered in the development of AI technology, which will hinder the further development and application of AI technology.

The bottleneck of artificial intelligence technology mainly includes the following aspects:

Data quality problem: artificial intelligence needs a lot of data to train and optimize the model, but there are many problems in real data, such as noise, inaccuracy, missing values and so on. These problems will lead to model instability and performance degradation, and then affect the application scope and effect of AI technology. Therefore, how to process and purify data is an important issue in the development of AI technology.

Computing power and storage problems: Artificial intelligence technology needs a lot of computing power and storage resources to complete tasks such as training and reasoning, but existing hardware devices are often difficult to meet these needs. Although special hardware such as GPU can improve the operation speed, it is expensive and impractical for ordinary users. Therefore, how to improve the performance of hardware equipment and reduce the cost is a difficult problem in the development of AI technology.

Complexity of computing models: Artificial intelligence technology needs to build complex computing models to achieve various tasks, but these models are often very complex, which makes it very difficult to train and optimize these models. This is also a bottleneck in the development of artificial intelligence technology.

These "bottlenecks" will affect the further development and application of artificial intelligence technology, because they make it very difficult to build more complex and accurate artificial intelligence models, requiring higher-level computing equipment and more efficient algorithms. In addition, these "bottlenecks" will also limit the application of artificial intelligence technology in some specific fields, making it impossible to give full play to its application in this field.

In order to overcome these bottlenecks, researchers are constantly exploring new algorithms and technologies to improve the training efficiency and performance of artificial intelligence models. For example, some researchers are trying to use more general and easy-to-use deep learning algorithms and more efficient algorithms and hardware devices to train artificial intelligence models. In addition, some researchers are also trying to solve the problem of data quality, such as replacing the traditional data collection method with manual labeling data to improve the quality and accuracy of data.