**Foreword:**
This article presents the latest research from the Future Intelligent Lab on artificial intelligence (AI) and its potential IQ in the future. The study introduces the concept that an intelligent system's intelligence level can be categorized into three distinct AI IQ types, each designed for different testing purposes. For these three AI IQs, the paper also outlines corresponding testing methods and mathematical formulas to evaluate their performance.
In our research, we discovered that when humans assess the level of AI development, the needs and goals vary significantly, leading to different evaluations of AI IQ. The first purpose is to determine whether an AI system or robot surpasses human intelligence. The second is to understand how smart a smart product is in serving humanity and at what cost. Based on this distinction, the Future Intelligent Lab proposes three types of AI IQ: General IQ, Service IQ, and Value IQ.
**0. Background**
With the rapid development of artificial intelligence around the world, especially after AlphaGo’s victory over the human Go champion Li Shihua in 2016, concerns about AI’s potential to surpass humans have grown. This has led to increased interest in quantitatively measuring the intelligence of smart products. While the Turing Test, introduced in 1950, remains one of the most well-known AI evaluation methods, it only determines if a machine can mimic human intelligence, not how intelligent it truly is. It is heavily influenced by human judgment and lacks a standardized approach for comparing different systems.
In 2015, a new version of the Turing Test called the "Visual Turing Test" was proposed to evaluate AI's image recognition capabilities more deeply. In 2014, Professor Mark O. Riedl introduced the Lovelace 2.0 test, which focuses on creativity, including tasks like writing novels, composing poetry, and creating art. However, existing tests still fail to provide a comprehensive, quantitative measure of AI intelligence across different domains.
To address these challenges, the research team proposed three types of AI IQ: General IQ, Service IQ, and Value IQ. These concepts are explained in detail below.
**1. Theoretical Basis: Standard Intelligent Systems and Extended von Neumann Architecture**
Evaluating the intelligence of both AI and human systems presents two major challenges: the lack of a unified model for AI and the absence of a common framework between AI and biological systems. To tackle this, the Future Intelligent Lab proposed a "standard intelligent model," integrating elements such as the von Neumann architecture, human intelligence models, and the DIKW knowledge management framework. This model describes AI and human intelligence in a unified way, treating every agent as a system capable of "knowledge acquisition, mastery, innovation, and feedback."
Building on this, the extended von Neumann architecture adds creative capabilities, allowing AI to generate new knowledge and rules, store them, and interact with the external environment through input/output systems. This architecture plays a key role in developing AI IQ metrics.
**2. Definition of Three Different AI IQs**
**2.1 AI General IQ**
Based on the standard intelligent model, the research team developed an AI IQ test scale. They tested over 50 AI systems, including Google, Siri, Baidu, Bing, and children aged 6, 12, and 18. Results showed that AI systems have improved significantly but still lag behind 6-year-olds in some areas. The AI General IQ measures the overall intelligence of an AI system, treating it as an equal agent in interactions with other agents in nature.
**2.2 AI Service IQ**
Most AI systems are designed to serve humans, and their service quality reflects their intelligence. Therefore, the Service IQ evaluates how well an AI system serves humans, considering factors like ethics, user experience, and real-time interaction. Unlike General IQ, Service IQ is tailored to the specific functions of smart products.
**2.3 AI Value IQ**
Value IQ measures the economic value of an AI system relative to its price. It helps users judge whether a smart product offers good value for money. This IQ combines intelligence evaluation with cost analysis, making it useful for consumers.
**3. Design of AI IQ Test Scales**
**3.1 General IQ Test Scale**
The General IQ test includes categories like knowledge acquisition, mastery, innovation, and feedback. It has evolved over time, adding new dimensions such as emotion recognition, dynamic image identification, and environmental awareness.
**3.2 Service IQ Test Scale**
Service IQ focuses on how well an AI system interacts with users, connects to the cloud, and adheres to ethical and legal standards. It evaluates real-time user support, safety, and energy efficiency.
**3.3 How to Calculate AI Value IQ**
The Value IQ formula is:
**IQAIV = (IQAIS / P) × 100**, where **P** is the product’s price.
**4. Conclusion**
By introducing General, Service, and Value IQ, the Future Intelligent Lab provides a comprehensive framework for evaluating AI systems. These IQs reflect different aspects of AI intelligence and help better understand the evolving relationship between AI and humans. Future work will involve applying these IQs to specific smart products like smart speakers, phones, and cars.
The Future Intelligent Lab is a joint research institute involving AI, internet, and neuroscience experts. Its mission is to develop AI IQ evaluation systems, conduct global AI IQ assessments, and promote the intelligent development of cities and industries.
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