Brain Intelligence: Go Beyond Artificial Intelligence Artificial intelligence (AI) is a key technology that supports daily social life and economic activities. It contributes significantly to Japan's economic growth and solves various social problems. However, current AI has limitations, such as being overly dependent on big data, lacking self-idea functions, and being complex. This paper proposes a new concept of general-purpose intelligence cognition technology called "Beyond AI," aiming to develop a Brain Intelligence (BI) model that generates new ideas about events without prior experience by using artificial life with an imagine function. The BI model will be demonstrated in automatic driving, precision medical care, and industrial robots. Current AI is mainly weak AI, designed for specific tasks. However, it lacks the ability to perform general cognitive tasks. This paper reviews recent weak AI algorithms and introduces the next-generation intelligence architecture, Brain Intelligence, which addresses the limitations of weak AI algorithms. The paper discusses various AI technologies, including natural language generation, speech recognition, virtual/augmented reality, AI-optimized hardware, decision management, deep learning platforms, robotic process automation, text analytics and NLP, and visual recognition. It highlights the limitations of current AI, such as the frame problem, association function problem, symbol grounding problem, and mental/physical problem. The BI model combines the benefits of artificial life (AL) and AI. It uses unsupervised learning methods and has the ability to understand concepts with a small database. The BI model network combines artificial life technology and artificial intelligence technology with memory function. It is designed to address the limitations of current AI by integrating various AI methods and improving the structure of current AI models using S-system. The paper concludes that the BI model can solve the issues of the frame problem, the association function problem, the symbol grounding problem, and the mental/physical problem. It proposes a super-intelligent brain function model that intends to discover problems itself and autonomously enhance its abilities.Brain Intelligence: Go Beyond Artificial Intelligence Artificial intelligence (AI) is a key technology that supports daily social life and economic activities. It contributes significantly to Japan's economic growth and solves various social problems. However, current AI has limitations, such as being overly dependent on big data, lacking self-idea functions, and being complex. This paper proposes a new concept of general-purpose intelligence cognition technology called "Beyond AI," aiming to develop a Brain Intelligence (BI) model that generates new ideas about events without prior experience by using artificial life with an imagine function. The BI model will be demonstrated in automatic driving, precision medical care, and industrial robots. Current AI is mainly weak AI, designed for specific tasks. However, it lacks the ability to perform general cognitive tasks. This paper reviews recent weak AI algorithms and introduces the next-generation intelligence architecture, Brain Intelligence, which addresses the limitations of weak AI algorithms. The paper discusses various AI technologies, including natural language generation, speech recognition, virtual/augmented reality, AI-optimized hardware, decision management, deep learning platforms, robotic process automation, text analytics and NLP, and visual recognition. It highlights the limitations of current AI, such as the frame problem, association function problem, symbol grounding problem, and mental/physical problem. The BI model combines the benefits of artificial life (AL) and AI. It uses unsupervised learning methods and has the ability to understand concepts with a small database. The BI model network combines artificial life technology and artificial intelligence technology with memory function. It is designed to address the limitations of current AI by integrating various AI methods and improving the structure of current AI models using S-system. The paper concludes that the BI model can solve the issues of the frame problem, the association function problem, the symbol grounding problem, and the mental/physical problem. It proposes a super-intelligent brain function model that intends to discover problems itself and autonomously enhance its abilities.