开源AI是对中央管理项目的严重挑战

据报道，DeepSeek的发行以领先模型成本的一小部分接受了培训，已将开源AI巩固为严重的挑战

The release of DeepSeek, which was reportedly trained at a fraction of the cost of leading models, has opened up new possibilities for open-source AI, according to Dr. Ala Shaabana, co-founder of the OpenTensor Foundation.

OpenTensor Foundation的联合创始人Ala Shaabana博士表示，DeepSeek的发布是在领先模型的一小部分接受培训的，它为开源AI开辟了新的可能性。

In an interview with Cointelegraph, Dr. Shaabana discussed the recent advances in open-source AI and how they compare to centralized projects. He also shared his thoughts on the future of AI and the role that open-source will play in its development.

Shaabana博士在接受Cointelegraph的采访时讨论了开源AI的最新进展以及它们与集中项目的比较。他还分享了他对AI的未来以及开源在其开发中发挥的作用的想法。

According to Dr. Shaabana, the release of DeepSeek, an open-source AI model that was reportedly trained at a fraction of the cost of leading models, hassolidified open-source AI as a serious challenger to centrally managed projects.

根据Shaabana博士的说法，DeepSeek的发行是一种开源AI模型，据报道，该模型的培训是领先模型的一小部分，Hassolidifiend开源AI是一个认真的挑战者，可以进行集中管理的项目。

“The entire paradigm of centrally managed AI, which costs tens of billions of dollars to develop and train, is being called into question by DeepSeek,” the OpenTensor Foundation co-founder told Cointelegraph. “Open-source AI is now capable of achieving performance that is on par with — and in some cases, even exceeds — the best centralized models.”

Opentensor Foundation联合创始人告诉Cointelegraph：“中央管理的AI的整个范式要花数十亿美元的开发和训练。” “开源AI现在能够实现与最好的集中式模型相吻合的性能 - 在某些情况下甚至超过了最佳的集中式模型。”

Dr. Shaabana attributed the rapid progress of open-source AI, and the narrowing of the gap between centralized systems, to a procedural shift in academia, which is now requiring researchers to include their code with their papers in order to submit to academic journals for publication. This requirement, he said, is making it easier for other researchers to build upon existing work and to develop new models more quickly.

Shaabana博士将开源AI的快速进步以及集中式系统之间差距的缩小归因于学术界的程序转变，该过程现在要求研究人员将其代码与论文一起包括在内，以便屈服于学术期刊以征求学术期刊发布。他说，这一要求使其他研究人员更容易建立现有工作并更快地开发新模型。

“Another factor that is contributing to the success of open-source AI is the increasing regulatory burden on centralized AI projects,” Dr. Shaabana added. “For example, there are now potential geographic restrictions on data due to geopolitical tensions, which could place further burdens on centralized AI projects.”

Shaabana博士补充说：“有助于开源AI成功的另一个因素是集中的AI项目的监管负担增加。” “例如，由于地缘政治紧张局势，现在对数据有潜在的地理限制，这可能会对集中的AI项目造成进一步的负担。”

The costs and regulatory burdens resulting from increased regulations would widen the performance gap between centralized and open-source systems, which are not susceptible to those constraints, he explained.

他解释说，由于法规增加而产生的成本和监管负担将扩大集中式和开源系统之间的性能差距，而这些系统不容易受到这些约束的影响。

“We are already seeing the effects of this dynamic in the market, with several open-source AI startups attracting large investments and centralized AI projects struggling to keep up,” Dr. Shaabana said. “I believe that this trend will continue in the future, and that open-source AI will play an increasingly important role in the development of AI.”

Shaabana博士说：“我们已经看到了这种动态在市场上的影响，几家开源AI初创公司吸引了大型投资和集中的AI项目，他们努力跟上努力。” “我相信这种趋势将在未来继续，开源AI将在AI的发展中发挥越来越重要的作用。”