Deeply ill develops artificial intelligence models that improve their self -performance

Deep SIK works with the University of Tsinghua to reduce the training needed for artificial intelligence models in an effort to lower operating costs. After the emerging Chinese company “Deep Seck” made the markets disappear by offering a low -cost -logical reasoning model in January, he worked with researchers from the educational institution in Beijing in a research article that deals with a new approach in reinforcement learning to improve the efficiency of models. The new approach aims to help artificial intelligence models increase the dedication to human preferences by giving rewards in exchange for the most accurate and understandable answers, according to what the researchers wrote. The strengthening of learning has been effective in accelerating the rate of artificial intelligence tasks in limited applications and fields. Nevertheless, some challenges emerged before expanding its use to include more public applications, the problem that the “Deep Seck” team tries to solve by what it calls “self-principle-criticism setting”. This strategy has exceeded current methods and models in many standard standards, and the results show an improved performance with the use of lower computer sources, according to the research article. Artificial intelligence is self -evolutionary, “deeply ill” is called “grams” – in short “general reward modeling” – and according to the company is offered by Open Source. The other artificial intelligence developers, such as the “Ali Baby Holding”, a giant technology and “Openai”, based in San Francisco, are to investigate new horizons to improve the capabilities of logical reasoning and self -development of artificial intelligence models while performing in real time tasks. “Mita Platforms” – its headquarters in Menlo Park, California – Lama 4, launched the latest collection of artificial intelligence models during the weekend, and indicated that it was the first modal of its development using the structure ‘mix of experts’. Deep sick models are largely dependent on the structure of the “mix experts” to increase the efficiency of resources, while “Mita” compares its newly released models to those developed by the Chinese Start -Up business in Hangzhou. Deep Seck did not determine the possible date for the launch of its new basic model.