開源AI是對中央管理項目的嚴重挑戰

據報導，DeepSeek的發行以領先模型成本的一小部分接受了培訓，已將開源AI鞏固為嚴重的挑戰

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的聯合創始人Ala Shaabana博士表示，DeepSeek的發布是在領先模型的一小部分接受培訓的，它為開源AI開闢了新的可能性。

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的採訪時討論了開源AI的最新進展以及它們與集中項目的比較。他還分享了他對AI的未來以及開源在其開發中發揮的作用的想法。

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.

根據Shaabana博士的說法，DeepSeek的發行是一種開源AI模型，據報導，該模型的培訓是領先模型的一小部分，Hassolidifiend開源AI是一個認真的挑戰者，可以進行集中管理的項目。

“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聯合創始人告訴Cointelegraph：“中央管理的AI的整個範式要花數十億美元的開發和訓練。” “開源AI現在能夠實現與最好的集中式模型相吻合的性能 - 在某些情況下甚至超過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.

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出版品.他說，這一要求使其他研究人員更容易建立現有工作並更快地開發新模型。

“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博士補充說：“有助於開源AI成功的另一個因素是集中的AI項目的監管負擔增加。” “例如，由於地緣政治緊張局勢，現在對數據有潛在的地理限制，這可能會對集中的AI項目造成進一步的負擔。”

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.

他解釋說，由於法規增加而產生的成本和監管負擔將擴大集中式和開源系統之間的性能差距，而這些系統不容易受到這些約束的影響。

“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博士說：“我們已經看到了這種動態在市場上的影響，幾家開源AI初創公司吸引了大型投資和集中的AI項目，他們努力跟上努力。” “我相信這種趨勢將在未來繼續，開源AI將在AI的發展中發揮越來越重要的作用。”