The AI System: Revolutionizing Investment Strategies with Cutting-Edge Technology
AI1.0 – Rule-based AI
- Time Period: First half of 2022
- Technical Foundation: AI 1.0 relied on predefined rules and logic, simulating decision-making through hard-coded “if-then” statements.
- Application Scenarios: Early expert systems and chess programs exemplify AI 1.0, performing specific tasks with high efficiency.
- Limitations: The system required manually written rules, making it challenging and time-consuming to handle complex problems.
AI2.0 – Knowledge-based AI
- Time Period: Second half of 2022
- Technical Foundation: AI 2.0 used knowledge bases to enhance reasoning with expert input.
- Application Scenarios: Applied in medical diagnosis (e.g., MYCIN) and complex decision-support systems.
- Limitations: Required substantial human input and struggled with dynamic environments.
AI3.0 – The Rise of Machine Learning
- Time Period: First half of 2023
- Technical Foundation: AI 3.0 developed machine learning algorithms, using data to train models and extract patterns.
- Application Scenarios: Expanded to speech recognition, image processing, and automation, with neural networks as a key milestone.
- Limitations: Performance depends on data quality, with growing transparency issues in models.
AI4.0 – The Rise of Deep Learning AI
- Time Period: Second half of 2023
- Technical Foundation: AI 4.0 relies on deep learning models and large-scale data processing.
- Application Scenarios: Used in autonomous driving, voice assistants, image recognition, and natural language processing.
- Limitations: Deep learning models are “black box” systems, difficult to interpret, and require vast labeled data.
AI5.0 – Early Exploration of AGI
- Time Period: First half of 2024
- Technical Foundation: AI aims for Artificial General Intelligence (AGI), enabling autonomous adaptation to various tasks and environments.
- Application Scenarios: AGI shows adaptability in fields like autonomous driving and medical assistance, learning across multiple domains.
- Limitations: True AGI faces challenges in scalability, learning efficiency, and ethical concerns.
AI6.0 – Autonomous and Multimodal
- Time Period: Second half of 2024
- Technical Foundation: AI 6.0 will feature fully autonomous learning systems and multimodal AI integrating multiple data sources.
- Application Scenarios: Used in intelligent robots, cross-domain systems, and advanced decision-making, with breakthroughs expected in healthcare, law, and science.
- Challenges and Prospects: Faces challenges in autonomy, ethics, regulation, and societal acceptance.