Group 1 - The core idea of "Artificial Intelligence +" represents a significant leap from "Internet +" to a new era of intelligent applications, enabling machines to analyze and make decisions autonomously, leading to a qualitative change in various industries [1][2] - "Artificial Intelligence +" is closely tied to improving quality of life, with applications in personalized home, travel, and health management, enhancing efficiency in areas like medical billing and food safety [1][2] - The transition to an intelligent economy requires the cultivation of new business models and the promotion of intelligent development across all sectors, including agriculture, where there is a notable shortfall in AI application [2] Group 2 - The current application of artificial intelligence is primarily concentrated in the secondary and tertiary industries, with the primary industry lagging behind, necessitating the development of smart agricultural equipment to enhance productivity [2] - Talent development is crucial for the advancement of "Artificial Intelligence +", as the current constraints in computing power hinder its widespread adoption, highlighting the urgent need for upgrading domestic computing technologies [2] - The increasing scale of "Artificial Intelligence +" necessitates enhanced security capabilities to address social challenges and risks associated with algorithmic biases and data governance [3]

Group 1 - The core idea of "Artificial Intelligence +" represents a significant leap from "Internet +" to a new era of intelligent applications, enabling machines to analyze and make decisions autonomously, leading to a qualitative change in various industries [1][2] - "Artificial Intelligence +" is closely tied to improving quality of life, with applications in personalized home, travel, and health management, enhancing efficiency in areas like medical billing and food safety [1][2] - The transition to an intelligent economy requires the cultivation of new business models and the promotion of intelligent development across all sectors, including agriculture, where there is a notable shortfall in AI application [2] Group 2 - The current application of artificial intelligence is primarily concentrated in the secondary and tertiary industries, with the primary industry lagging behind, necessitating the development of smart agricultural equipment to enhance productivity [2] - Talent development is crucial for the advancement of "Artificial Intelligence +", as the current constraints in computing power hinder its widespread adoption, highlighting the urgent need for upgrading domestic computing technologies [2] - The increasing scale of "Artificial Intelligence +" necessitates enhanced security capabilities to address social challenges and risks associated with algorithmic biases and data governance [3]

Group 1 - The core idea of "Artificial Intelligence +" represents a significant leap from "Internet +" to a new era of intelligent applications, enabling machines to analyze and make decisions autonomously, leading to a qualitative change in various industries [1][2] - "Artificial Intelligence +" is closely tied to improving quality of life, with applications in personalized home, travel, and health management, enhancing efficiency in areas like medical billing and food safety [1][2] - The transition to an intelligent economy requires the cultivation of new models and business formats, with a focus on integrating AI across all sectors, including agriculture, where there is a notable shortfall in application [2] Group 2 - The current application of AI is primarily concentrated in the secondary and tertiary industries, with the primary industry lagging behind, necessitating the development of smart agricultural equipment to enhance productivity [2] - Talent development is crucial for the advancement of "Artificial Intelligence +", as the growth of AI applications is heavily dependent on computational power, which is currently constrained [2] - The safety and governance of AI applications are becoming increasingly important, with a need for robust measures to address risks such as algorithmic bias and data security [3]