1. Essential positioning: complementarity between the capability layer and the carrier layer
1. AI Ability: The "brain" of breakthrough cognition
- Definition: The core capabilities of AI (such as generalization reasoning of large language models and multimodal perception) originate from algorithm innovation, massive data and computing power breakthroughs, and its essence is to expand the boundaries of human cognition.
- Limitations: AI with only "capabilities" is like an unassembled chip that cannot be directly embedded in real scenarios, and functions need to be implemented through an engineering tool chain.
2. Development framework: "scaffolding" for the implementation of the project
- Role: Frameworks such as TensorFlow, PyTorch, Dify provide standardized interfaces, resource management, distributed training and other capabilities, and are essentially transforming abstract intelligence into programmable and reusable technical components.
- Reasons for relying on traditional engineering capabilities:
- System stability: single point of failure needs to be avoided through modular design of software engineering;
- Performance optimization: The model inference speed depends on traditional optimization methods such as parallel computing and memory management of the underlying code;
- Security compliance: Data privacy protection, model auditing, etc. need to rely on a mature engineering security system.
2. Dynamic relationship: the leap from "laboratory" to "industrial"
1. Experimental stage: AI capability-led
- The core goal is to verify the validity of the model, and engineering requirements only revolve around rapid iteration (such as Jupyter Notebook prototype development).
2. Production stage: The weight of engineering capacity increases
- Scenario adaptation: It needs to encapsulate APIs through frameworks, support microservice architectures, and meet high concurrent calls (such as e-commerce customer service robots need to deal with "Double Eleven" traffic peak);
- Long-term operation and maintenance: Model monitoring (such as drift detection), A/B testing, grayscale release and other depend on DevOps and MLOps engineering practices.
3. Typical contradictions and collaborative strategies
1. Contradiction: Innovation ability vs. Engineering certainty
- "Uncertainty" of AI: There is randomness in the output of large models, which conflicts with the requirements for certainty in industrial scenarios;
- Engineered "noise reduction": Constrain AI behavior through traditional means such as rule engines (such as Dify's sensitive word filtering), output templates, etc.
2. Collaborative case: autonomous driving system
- AI capabilities: Visual models identify road obstacles in real time;
- Engineering framework: ROS (robot operating system) coordinates sensor data flow and issuance of control instructions;
- Traditional engineering capabilities: real-time code optimization (such as C++ underlying driver), functional safety certification (ISO 26262).
4. Future evolution: Two-way penetration and capacity integration
1. AI’s “feedback” to engineering capabilities
- AI-assisted coding: GitHub Copilot reduces duplicate code writing in the use of the framework;
- AI-driven automation testing: generate test cases based on LLM to improve framework stability.
2. "Enhancement" of AI by engineering capabilities
- Computing power virtualization: Kubernetes cluster scheduling optimizes GPU utilization, reducing the cost of large model training;
- Edge computing framework: TensorFlow Lite embeds AI capabilities into IoT devices to expand application boundaries.
5. Suggestions for refactoring the developer's capability model
1. Training of “T-type talents”
- vertical depth: Understanding the underlying principles of AI (such as attention mechanisms, diffusion models);
- Horizontal breadth: Master software engineering full-link skills (such as containerized deployment, CI/CD).
2. Toolchain selection principle
- Lightweight frameworks (such as Dify): Quickly verify AI creativity and lower engineering barriers;
- Heavy-duty frameworks (such as Kubeflow): meet complex enterprise needs, but need to be applied to engineering teams.
Therefore, AI capabilities and engineering frameworks are not opposites, but symbiosis between "content" and "container".
Future technological competition will focus on the seamless fusion of the two - unleashing AI possibilities with engineering certainty and reverse the evolution of the engineering paradigm with AI innovation.
- SHA hash encryption
- MD5 Encryption
- Unix timestamp conversion
- Base64 encoding
- URL codec
- Online IPFS Storage
- Ethernet ETH Unit Converter
- Ethereum address conversion
- Image extraction text
Link：/farwish/p/18756165