AI Progress Assessment
Assessing AI Progress Through Entropy A Multidimensional Study on China's Provincial-Level Competitiveness in Artificial Intelligence
This comprehensive analysis examines China's provincial AI development, revealing growth trends, regional disparities, and key factors driving competitiveness. Leverage these insights to inform your strategic AI initiatives.
Key Findings: Regional AI Development Dynamics
Our study highlights significant regional variations and overall growth in China's AI landscape, offering critical insights for targeted investment and policy formulation.
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Increase in China's AI development index (2017-2021)
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Eastern Region Average AI Index (2017-2021)
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Growth in Central Region AI Development (5 years)
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Provinces with High AI Development Level
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
AI Definition
Indicator System
Entropy Method
LSTM for Missing Data
Core Concepts of New Generation AI
The academic community defines a new generation of artificial intelligence (AI) as technologies developed since 2006, primarily driven by big data and deep learning. This approach emphasizes end-to-end feature-based learning, reducing reliance on prior knowledge and expanding AI applications across various industries. This paper defines new generation AI as theoretical research based on technological innovations like big data intelligence, deep learning, human-machine hybrid intelligence, autonomous collaboration, decision-making, and cross-media perception computing, with algorithms as the core. The aim is to improve perception recognition, knowledge computing, and human-computer interaction capabilities.
Robust AI Evaluation Index System
A comprehensive evaluation index system for China's new generation AI development was constructed using 13 sub-indicators across four dimensions: development scale, economic benefits, innovation input, and scientific and technological output. This system measures the overall development level of AI across provinces, municipalities, and autonomous regions. Examples include 'Number of companies with R&D institutions', 'Software product revenue', 'Internal expenditure on R&D funds', 'Number of patent applications', and 'Number of incubation platforms'.
Objective Weighting with the Entropy Method
The entropy method is utilized to assess the multicollinearity of variables and determine the objective weights of indicators. It involves removing the dimension of each indicator through min-max standardization, calculating the weight of each index (Pij), information entropy (ej), variation index (dj), and finally the comprehensive score (Si) for each evaluation object. A larger entropy value indicates a closer relationship between variables.
LSTM for Enhanced Data Accuracy
Missing data in the panel dataset (2017-2021) was interpolated using a Long Short-Term Memory (LSTM) dynamic algorithm. LSTM, a type of recurrent neural network, excels at time series feature extraction through a gating mechanism (forget, input, and output gates). It retains historical information, filters current valid data, integrates new and old information, and generates the final hidden state. The process involves converting time series to a supervised learning format, marking missing data with a binary mask, and using Min-Max standardization. Training uses weighted mean square error with an Adam optimizer.
Overall AI Development Growth
44.2% Increase in China's AI development index (2017-2021)Enterprise Process Flow
Define New-Gen AI
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Establish Evaluation Index System
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Collect Provincial Panel Data (2017-2021)
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Handle Missing Data (LSTM)
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Calculate Indicator Weights (Entropy Method)
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Compute Provincial AI Development Index
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Analyze Spatiotemporal Trends & Differences
| Region | Average Index | Key Characteristics |
|---|---|---|
| Eastern Region | 0.2985 | |
| Central Region | 0.1203 | |
| Western Region | 0.0628 | |
| Northeast Region | 0.0706 |
Case Study: Guangdong's AI Leadership
Challenge: Many provinces in China struggled with disparate AI development levels and fragmented policy implementation.
Solution: Guangdong province, a key economic hub, implemented robust strategies focusing on innovation input and economic benefits from AI. This included significant investment in R&D, fostering a high number of AI companies, and promoting technology market turnover.
Outcome: As a result, Guangdong consistently ranked among the top provinces in AI development. By 2021, its AI development index reached 0.758, showcasing the highest level nationwide and demonstrating the tangible impact of strategic AI investment and policy support.
Estimate Your AI ROI
Project the potential efficiency gains and cost savings your enterprise could realize by strategically implementing AI solutions.
Projected Annual Savings
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Annual Hours Reclaimed
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Your AI Implementation Roadmap
A structured approach to integrating AI, tailored to maximize impact and minimize disruption within your enterprise.
Phase 1: Discovery & Strategy (2-4 Weeks)
In-depth analysis of current operations, identification of AI opportunities, and development of a bespoke AI strategy aligned with business objectives. Includes data readiness assessment.
Phase 2: Pilot Program Development (4-8 Weeks)
Design and implementation of a targeted AI pilot in a low-risk environment. Focus on quick wins and measurable results to validate the AI solution and gather stakeholder feedback.
Phase 3: Scaled Implementation (8-16 Weeks)
Gradual rollout of the AI solution across relevant departments, integrated with existing systems. Comprehensive training for users and continuous performance monitoring.
Phase 4: Optimization & Expansion (Ongoing)
Post-implementation review, fine-tuning of AI models for peak performance, and exploration of new AI applications and expansion opportunities across the enterprise.
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