The Development Obstacles of Artificial Intelligence（PlatON）
The main obstacles to the development of artificial intelligence include:
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Data privacy and regulation: The development of artificial intelligence relies on a large amount of data, including personal and business data. The processing and storage of these data must comply with relevant privacy regulations, while also ensuring the security and integrity of the data. However, current regulations may not be sufficient to meet the increasingly complex needs of artificial intelligence systems, especially in areas such as finance, healthcare, and education. Some countries or regions have already proposed stricter privacy regulations, which may have an impact on the development of artificial intelligence. Therefore, the focus of artificial intelligence has shifted from a focus on AI based algorithms to a focus on big data architectures that ensure security and privacy. The isolation of data and the protection of data privacy are becoming the next challenge in the field of AI.
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High training costs: Training artificial intelligence systems requires a large amount of computing resources and data. Although advances in hardware and software have been driving the annual cost of artificial intelligence training to decrease by 37%, the faster growth of artificial intelligence models (10 times annually) has led to a continued increase in the total training cost of artificial intelligence. This leads to high training costs, especially when large-scale model training is required. For many businesses and institutions, this can be a significant economic burden. To address this issue, some companies are exploring the use of cloud computing and distributed computing resources, as well as reducing costs through open source models and code.
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Centralization: Many applications and developments of artificial intelligence are being concentrated in a few companies, and a few technology giants monopolize the upstream of data by providing services to consumers, obtaining unprecedented amounts of data and training high
Integrating AI models into their ecosystem further increases the dependence of users and other companies on technology giants. These companies have significant advantages in data, algorithms, and computing power, enabling the development of more advanced artificial intelligence systems. However, this trend towards centralization also brings some issues, such as data privacy, security, and fairness issues. In addition, this centralization may also limit the popularization and application of artificial intelligence, as many small businesses and institutions may not be able to access advanced artificial intelligence systems. -
Non democratization: The development and application of artificial intelligence may also lead to the problem of non democratization. Some companies may use artificial intelligence systems to automate certain jobs, resulting in changes in the job market. Although AI has made tremendous progress, its benefits have not been widely utilized, and artificial intelligence has not yet achieved democracyAnd there is a trend towards increasing concentration. This leads to issues of unfairness. Therefore, measures need to be taken to ensure the transparency and fairness of artificial intelligence systems, and to ensure that they can be widely accepted and used.